How to redesign an $800M-a-year software giant for the AI era - Dan Rogers [Asana]

0:00Dan Rogers:Now, I have a net new ARR that's coming from my AI products. What we decided to do, I think, is very bold and maybe a first in the industry. The modern team is no longer just humans. For an AI agent to be useful in the enterprise, they need first and foremost. Today on Billions, I'm sitting down with Dan Rogers, CEO of Asala. 13 months ago, Dan took over a company with$800 million in revenue, 18 years of history. Since then, he's pushed the operating margin to a company record, spent$75 million on an acquisition, rebuilt the leadership team, and also opened the US government market. Dan, thanks a lot for coming on the show.

0:39Dan Rogers:Great to be here. Nice to see you, Guillaume. Yeah, I'm quite excited to understand. So about a year and a half ago, I stepped down from the CEO and became chairman. On your side, it's the other way around. You're coming into a mature company and taking the lead. so how exactly like do you approach this new role this new function especially in a world where everything has been driven by ai yeah so you know this is not my first rodeo it turns out my last two companies i was uh you know i had the fortune of working with great founders who'd brought in as a president at rubric and then as the ceo of launch darkly both founder driven companies.

1:20Dan Rogers:So it's something I'm very used to. So I'd say, you know, I guess the playbook or the framework is you have two ears and one mouth, so you should be listening twice as much as you talk, trying to figure out exactly what's what. And, you know, sometimes I think of it like, you know, in Harry Potter, they have this assorting hat. And I think you kind of have to do that with all of the norms, the procedures, the decisions, you try to put them in one of the hats, Maybe one of the hats is, you know, this is a great idea, fully understand why we do this. Another hat might be, you know, I need to learn a lot more about this.

1:58Dan Rogers:There's something in this that I need to understand more, I just don't know enough. And then, you know, the final hat might be, I'm not sure this thing is going to serve us anymore. Maybe it's something that we can cast aside and isn't fit for the next wave of what we're trying to do. so there's some intensity of trying to figure out you know which things go into which hats i think is uh you know part of this thing that you need to be doing and then soaking yourselves with customers and so you know when i arrive i definitely uh i like to do like a customer tour you know i had 100 customers in 100 days would be my ideal idea but that's really again getting as much external context as you can because after a couple of months you can become very internally orientated you can become very uh steeped in the culture in the decisions that have already been made but as you know if you do what you've always done you get what you've always got so you need to be very mindful of uh the context how things are changing what customers are saying what language they're using and how that matches up to everything you have so when you do all of that i think you get really the best of both worlds which is taking all the brilliance that the founders have all the decisions that they've made and building on the shoulder of giants really but bringing your own unique twist that's very custom informed and to your points i don't remember was saying that to jeff bezos but it's like you have enough good ideas to ruin the business or something like this so sometimes you have to focus and yeah yeah so i think the enemy of great in this example would be coming in 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 um sharp before you uh before you go live with it in mckinsey they call it um 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 going to transform the world it's great you have a big macro level view and then you need to come in and kind of say well this is 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 uh 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 very self-controlled on what your inputs are and how you're formulating those outputs yeah and you mentioned the the 100 customers in 100 days when you join a company and you have to line up, let's say, this 100 meetings, how exactly do you decide which customers you're going to meet?

5:03Do you actually 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. How exactly do you decide who you're going to meet and how do you structure each interview?

5:20Dan Rogers:Yeah, exactly. So I think having a diversity is very important. a diversity 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 a 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?

5:56Dan Rogers: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 to go they tend to go their own direction as well but i love it when you know customers open up their screens and kind of 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 i let's kind of back to this iterative porpoising idea i love going deep and so as deep as we can go the better awesome and 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?

7:15Dan Rogers:Yeah, well, if you start back in the beginning, 18 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's doing what by when and how we're going to 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.

7:52Dan Rogers: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 14 ,500, 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 type of Asana. But the change that is afoot is that the modern team is no longer just humans.

8:34Dan Rogers: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 chat bot 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.

9:21Dan Rogers: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? 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 at the right moments, making sure that they can prove things and that it's fully governable.

10:09Dan Rogers: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. So your view is down the line to kind of have agents perform as many tasks as possible and get all the context from Asana while always keeping a human in the loop to validate that the work has been done properly, that the project is going forward, etc. 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.

10:51Dan Rogers: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 uh so it's very uh lucky to be on that conversation with agentic work management these teammates make themselves available to you as you're working in asana so it might say hey i notice 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 you want me to help?

11:25Dan Rogers: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 pre-built 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.

12:01Dan Rogers:Now 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's kind of a game changer from where we've been.

12:38Dan Rogers:where we've been in this evolution was single player interaction with models. Good, you know, helpful from a personal perspective, but it's just 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 want to 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 going to affect the way you recognize your revenue. So you're going to involve your finance team.

13:14Dan Rogers:You've got some engineers that need to work on it. You probably have some folks, you know, product management needs to describe this new thing to your customers in turn, et cetera. So let's say there's like five or six parties. Just for a single change in your product functionality, it's really the agents that we want to do a lot of the coordination. It's the agents we want to figure out. Okay, well, this is probably the 25 tasks that are going to need to happen. And by the way, I'm going to need to hand it over in this way. And realistically, we should say this is going to take six weeks. It's one of the agents you want to kind of create that based on everything you already know.

13:45Dan Rogers:The humans, meanwhile, you want to say, yeah, I want to approve this. I want to take a look at this. No, this is the way we like to do this. This is the way that things are done here. This is our taste that we want to perform. And then at the end, when it's all finished, it's the human that kind of says yes this met our specification great and now we can do it even better the very next time so that's kind of human agent operating system in action and for the models that you are using are you leveraging like existing models from companies or have you like also built in your own model directly well as i would say from a model perspective they become quite interchangeable so we're very focused on really the kind of equality and performance of those agents for our customers, which actually does mean certain tasks get routed to certain models based on their efficacy of delivering them.

14:37Dan Rogers:And so under the hood, yes, we have interchangeability of many of the models. But we also work with the foundational models, because some people don't actually want to work in the canvas of Asana, they want to work in the canvas of their chat screen. And so for things like, you know, Codex, for things like Claude, for things like, etc, we have this MCP integration where if you are an asana customer and you're working with one of those foundation models you can actually view tasks hierarchy project and you can talk to asana really through those chat discussions and not even have to come into asana and so we have a very nice relationship as they kind of fold into the into that tapestry the reason i was asking is because um i think the q3 uh results were actually like really great and uh i think ai teammates from what i saw when from 17 % to like 25 % of net new ARR, which is huge and means like the adoption is actually like really, really nice.

15:39Also, what I've seen is like you're putting AI in most, if not every paid tier without changing prices, which means that you are also absorbing like potentially a token cost. So can you like maybe walk me through like, because for me, it's the same, you know, like obviously built a software company, margins were pretty much like uh you know like insane and with uh ai token obviously you need to make some sort of arbitrage but it feels like you've already managed to still get like a really good margin on top of the input cost of token so maybe you can walk us through like your strategy

16:18Dan Rogers:regarding tokens and how you manage it yeah so firstly um let's talk about the success of ai teammates as you said uh now 25 of our net new arr is coming from ai products which is ai teammates in ai studio in fact for our largest customers as well most of 25 of the new deals that we do with them are ai attached deals to them so yes we are seeing that people have a new way of working they want to bring ai into their core workflows their business critical workflows so and long may that trend continue is very good for Asana. But then a little bit about how that works from a token usage and a margin perspective.

16:58Dan Rogers:What we decided to do, I think is very bold and maybe a first in the industry. I'm sure someone would tell me if it's not. But we decided that we think the right unit of charging or unit of measure for how effectively AI is performing in these workflows is the fulfillment of a request. So we decided to create a new meter that we're calling request. Why is that bold? Most other, you know, SAS models are charging on credits or tokens. And they say, well, to perform this is 5 ,000 credits. To do this is 2 ,000 credits. You're like, I'm not sure if it's going to be more like this. Okay, well, in that case, it's 3 ,725 credits to perform.

17:36Dan Rogers:Very hard to keep a running ledger a priori. So before you do it, of what tokens are going to use, what credits are going to use. And we kind of said, don't worry, we'll obfuscate all of that to the customer. All you need to know is if you've asked this agent to do something, to perform some action, and it performs that action, ping, that's a request. And so we think it's a much more natural way of describing your interaction with the agents. And so, yeah, we think we're very bold in kind of coming to that kind of meter and obfuscating all of that complexity under the hood. The customer and customer shouldn't have to think about which model's being used, how many tokens it can share.

18:14Dan Rogers:It's just how I got the job done. But that's bold. The second part that's bold is we kind of said, we've had six months of experience now with teammates and with studio. And what we realize is that customers that get to a certain amount of usage become very sticky. But the hardest part is getting them to that amount of usage because of discovery. And so what we said is instead of having to buy something and imagine the value that you're going to get, Why don't we reverse that? Why don't we give them the value? And then eventually they'll buy because they're achieving so much value. So actually, we've actually given all users, as of today, so that's why it's a fairly formidable day for us.

18:54Dan Rogers:We're giving all users five requests per user per month, so that they become very accustomed to getting the value from these agents. We literally want our millions of users to be working with these agents and not just discover it through some procurement decision that you have to make, but actually get used to working with these agents because this is a new way of working. So I think we've been bold on two fronts, both on which meter we've decided, but also in how we're going to seed this. And as you suggest, wow, that is a very under the hood, a token consumptive idea. And so, yes, we have this made, again, the financial decision that that's worth it to do for us.

19:34Dan Rogers:we're being very good at our operating efficiency in kind of the rest of the business and that allows us to make these kinds of investments but yeah you can think of it as a sales and marketing investment maybe you think of it as a as a freemium model which is you know get something for free get using it and then because you enjoy it so much and i think it's very different than how everyone else has approached this so we're pretty proud of that yeah i think it's really smart and down the line like the more people see the value the easier it will be for them to explain why they need to pay for it and how much productivity they're gaining, how much less time they can spend on a task.

20:08And also curious because I think you made like a pretty big acquisition and you kept it like with its full brand and as a kind of like separate product. The acquisition, it was like Stack AI, if I remember correctly. So can you walk us through like the strategy you have in mind when it comes to buy versus build? And also like how exactly like what's your plan for the future in terms of integrations?

20:34Dan Rogers:Well, in general, as I described earlier, I'm very customer orientated. And as a leadership team, we're very externally orientated. So we have a strong sense of what we want to build next. We have a very sharp point of view about where the market is going and what we need to build. And so the build versus buy decision is pretty easy. It's against our roadmap because we are very sharp in what we want to build. Can we accelerate that? And so we're not going to look at targets that come across our debt. That's not really how we do it. We kind of say, we know we want to build these 20 capabilities because our customers are asking for this and they will love it.

21:14Dan Rogers:It will solve their problems. Okay. So can we shortcut at the timeline from 18 months to six months and from 12 months. So wherever we can deliver value to our customers, what kind of open ears, I think, for what that could look like. And Stack really fell into that category. We had a workflow capability with AI Studio. What we noticed is our customers wanted to build even more powerful workflows that extended across multi-system and multi-agent. And Stack really was a perfect fit for that. Many hundreds of pre-built integrations but also the know-how and how to interact with agents, how to call upon the right LLMs at the right time, how to scan through and retrieve information from all those databases and some real expertise in pulling information from documents and PDFs in particular.

22:04Dan Rogers:And so those were all really rich capabilities that honestly we wanted. And we tried not to let them know how excited we were until after they joined. but now they're part of the Asana family. We're super happy with how that pilot business is trending. It's becoming our workflow engine for really all of our products and all our capabilities. And then you mentioned on keeping their brand. It's a great brand, sub-brand really. It's Stack AI by Asana, but it has meaning and I think it's even as a word, it invokes the right idea that a workflow is a stacking up of these processes connecting to these databases, to this information, to these LLMs.

22:47Dan Rogers:So it's super appropriate. It has that builder mindset, which is really where we are headed. So that's a little bit on our philosophy there. Because on our side, we've acquired three different companies from different sizes. The smallest one had three or four people, and the biggest one maybe 15. I'm curious, from your perspective, how exactly do you map out for team integration? Because I feel like sometimes the culture might be a little bit different and that it's always a struggle, you know, for companies when they went from a team of like tens of people and they jumped into an organization with thousands of people.

23:27So how exactly do you make sure that you keep the talent, you keep the same like innovation pace, etc. within like the absorption of a company? Yeah.

23:38Dan Rogers:And I'll credit one of our board members for maybe a little bit of thinking around this. So you can think of it as the biggies and the littleies, right? So for the biggies, in some sense, they're obvious. It's like, oh, we need to have a joint roadmap. Oh, we need to make sure that people have the right organization chart and all this kind of stuff. and a lot of people I think start with the biggies and it comes very maybe naturally from an intellectual perspective but the reality of the success of M &A is it's actually all about the little ease it's all about the wait do I have to change my email address do I need to sure do my benefits continue what do you mean I have two weeks where I don't get my benefits like all of the little ease is what will I think catch you if you're not careful so imagine a world where we actually start with the little ease And the little ease is literally what is the daily experience of the employee that is joining and of the, you know, the original Asanas.

24:36Dan Rogers:How will they perceive and experience this? What are the micro frictions that you can get ahead of? And so that's very much how we approach this. And I'm very pleased with how we did it, actually. So I'd say, yeah, biggies and little ease. But start with the little ease. Okay, that's great. There is a question I had when I was looking at Asanas numbers and the growth and everything. like everything is is running like smoothly like i feel like you're adopting ai like really quickly you've changed a lot you know like uh the roadmap and positioning like um you seem to be like super close to your customers and dedicate a lot of time to understand like what exactly you should build but if we look at the at the overall like macroeconomics with what happened with saspocalypse and pretty much like all the valuation for software companies getting like sliced down as a ceo yourself managing like a public company when you have your stock price going down or an up and kind of like moving both direction regardless of how great the company results are because obviously like beginning of the year went down when saspocalypse happened etc and now I feel like it's been going up like since May and it's like super cool like also but how exactly do you manage like this up and down as a CEO and also like regarding the employees because I'm assuming that everyone get pieces of stocks at some point and they understand you know like that there are value behind it so how do you keep like the team really motivated and also not too affected by you know like everything that's happening and that sometimes feels a bit like noise you know yeah i know so it's a great question question i asked myself uh quite a bit actually the way i make peace with it both for myself and then how i kind of uh maybe for myself and then i'll describe for the team so for myself it's very much control what you can control it's very much a be very peaceful about are you doing the right things are you following the right processes are you building a high performance team have you building the right products it's roadmap to the right direction so you can i think you can create a great internal story for yourself about you know whether you're doing the right things you know i watch tennis i don't know if you watch tennis but you know i just watched the you know us open and uh you know a lot of the tennis players that don't break through in the grand slams you know have to still tell themselves a story about are they doing the right things?

27:09Dan Rogers:Are they doing the right motions? If so, that's great. Then that alone is great. And you saw someone that just hadn't won in 10 years, just won two Grand Slams in the last three months because they had that mental mindset of celebrating the actions, celebrating the journey, and the outcome will follow if you're confident and have that self-belief and know that you are doing the right things, the outcome will come. So I think That's the internal talk track. Vis-a-vis the company, it's about finding those wins that may or may not be, in the short term, stock-related. Over the medium term and long term, I think stock does become a great barometer.

27:49Dan Rogers:In the short term, it can be, as you say, very affected by a macroeconomic circumstance. You need another barometer. Stock price alone is not enough. You have to create those barometers. What are the things you are going to celebrate this quarter. And so today, as an example, is a big celebration day for us. It's a big launch day. It's really the launch of moving from what was collaborative work management to agentic work management. That is a major milestone that we are going to be partying a lot around today. It's a big deal for us. And we know that that is a landmark for us doing the right things.

28:25Dan Rogers:And how that gets represented in the stock market on a given day, who knows? But we know we're doing the right things and we're doing the right things for our customers i forgot which investor was saying that i think it was uh someone at uh at sequoia but they were saying like when they look at a company they always ask themselves if any of the frontier model is getting better is that company more valuable or less valuable i think like on your side you've integrated with many models etc but how exactly like uh do you like uh build kind of your moat around um you know like this model that can get updated and at the same time also bring more value to your customer yeah it's uh it's interesting i'm glad that someone's from sequoia said it because i say the same thing i think that's really the you know the say is the test is like the models will get better?

29:20Dan Rogers:And is that a good thing for you? And so the way I kind of think about it is in the next phase, the piece that AI is going to be doing, yes, they're going to be generating very impressive answers, right? And they're going to be generating very impressive code. And that's great. But the models are becoming somewhat interchangeable. The value that's going to be created is turning that intelligence into action inside those workflows in a company. So for an AI agent to be useful in enterprise, they need first and foremost context. So it's like, what's the company trying to achieve? Who owns that? How's this been done in the past?

29:58Dan Rogers:What's already been decided? Which systems do I need to work with? Are there any rules of the road I need to follow? Which knowledge base should we use? Which teams do I need to involve? Then it needs a harness. So context plus harness. Harness is this kind of coordination work, which is taking action, involving those right people, making sure the sequencing is right making sure it's all governed observable and trainable and improvable so the enduring value for asana and for enterprise ai is not going to be in the raw output of the models which is going to get better and better it's actually how do you turn that intelligence with the harness into repeatable useful accountable process that makes the work flow.

30:44Dan Rogers:And so for me, it's a very easy answer to that question, which is Asana is on the right side of this equation. As the models get better, we will get better. And you see that in the performance every day of our AI teammates. You see it in the performance every day of our AI chief of staff and a slew of new products that we've launched today for IT service management, which we call ASM, for R &D teams, which we call command, for client delivery teams, which we're calling Asana client management, we have created bespoke solutions for them that is the workflow and the harness against the things that they care about, the jobs to be done that they care about the most.