Miro CEO on reaching 100m users + 250k orgs, permission to win, hybrid pricing, more | Andrey Khusid

So our mission is to empower teams to create the next big thing, and whatever progresses that mission is our bet. I think PLG is a channel. It's not a business model, especially today. I think it's a great channel for people to explore and play with your product, but to be strategically deployed inside the organization, you need a different set of capabilities. We are now at the very beginning of this multi-product business model experimentation. There are things that we know and there are things that we don't know. The things that we know is that historically we have this per-seat pricing model, and with AI, this per-seat pricing model can be quite vulnerable. We use Miroboard actually for the whole pricing history in the company. This is where it started, and every step of the evolution is mapped on that canvas, which is amazing because you always can go back and see what was that experiment, what was the outcome, why we did it, what we've learned and what not. You can't predict what things would play out tomorrow, where you got disrupted. So the skill of zero to one is more needed than ever before. My sense is it will be a constant zero to one for a lot of companies from now on.

Thank you.

Yeah. No, thank you for having me here. It's been a long time since we originally chatted to have this conversation, and I appreciate the patience because I was building for the last couple of years.

Yeah, for this second act to come and talk about this with you. So this is definitely a big, interesting evolution in software that is happening now. There are horizontal tools and vertical tools that were not much overlapping historically, but with AI, we see an opportunity to build deeper use cases faster. And while a lot of people really benefit from simple user experience and connected workspace that can support the work across different departments and different roles, people still need to achieve their goals. And to achieve their goals, they need a specified set of tools and capabilities that help them achieve the goals. And that's where we're trying to find the right balance while being the platform that connects everyone in the company and allowing for that seamless collaboration and major productivity gains. We try to go deep and support use cases, especially in product innovation, because that's where the most value is created.

Yeah, partially. So for example, prototyping or wireframing, we had the wireframing capabilities for maybe now eight years on our platform, and it was very manual. So you come to Miro and if you need to express your ideas, you would manually move those stencils and kind of create the visualization of your idea. Now with AI, you can prompt the canvas and you will get the app in minutes, and then you can immediately test that app or discuss that app with the team, modify that app. That definitely shifts a lot. Same with diagramming. For example, before you had to manually design your architecture or you have a code base and then you need to document the code base. You would go and you would manually visualize that, and it takes hours of engineers to reflect the code base in the documentation system. Now it takes a few minutes with MCP to visualize the code base and document and then discuss it. So definitely AI is helping, but you can't just rely on AI. You need a set of those fundamental capabilities that AI is working with.

So yeah, it's been great that we've built over time a bunch of those fundamental capabilities, but then layering AI just accelerates those jobs to be done like 10x, 100x.

It's a great question. I think historically what we saw with our platform is that we saw that kind of makers movement because our platform is not a specialist tool. It's more a generalist tool, and designers were kind of thinking product, and engineers were thinking design, and product marketers were also kind of thinking their use cases on Miro and going beyond those use cases obviously. So in general, the idea of this blended role of maker, if you will, was always prevalent on our platform, and I think it's only amplified now because with AI, as an engineer you can go and prototype an interface, correct? You can upload the screenshot of your existing system and ask AI to help you add certain features to that. Or as a product manager, you can go and create the product story that you want to launch for customers, as before you had to partner with other folks to figure this out. So I mean, it's blending more, and we are trying to bring those capabilities that help individuals to be more productive and empower those maker teams more.

Yeah.

Yeah, exactly. And I think it's beautiful actually because with a smaller team you can achieve so much more today than a few years ago, and it creates the opportunity for teams to move faster and to play their strengths as you say way more. It's an incredible shift that we're observing today.

Yeah. It always kind of goes back and forth. So you always have an idea or intuition around direction, and you say okay, this is the opportunity. And then you go and look, okay, but what's the market look like today? Who is in that market? How that market we anticipate might develop? What's the buyer patterns? What's the users patterns? Are buyers trying to buy suite products or are they buying best-of-breed products? Are the buyers buying AI solutions or are the buyers buying non-AI solutions? It's just like you need to observe those behaviors and understand how to fit, because product-market fit exercise is, my belief, more about the market rather than about the product, because we all know amazing products that were built before the market was ready, or after the market got saturated, and they never got traction. So it's very important that whatever you build fits the market wave. But again, I think it's constant iteration, understanding the trends and the demand, what's the biggest problem to solve today, and then ensuring that your business model, that your distribution model, that the structure of your product matches that need. Because for example, we were a best-of-breed product for a long time, and in 2022 the market shifted to best-of-suite. So everyone tried to kind of consolidate and buy one platform that rules them all, and if you are best-of-breed, you will have a quite challenging situation. That's where we for example decided to move to a best-of-suite solution and not just be a horizontal suite, but be a suite that is oriented on innovation workflows to help teams to move from discovery to definition to delivery. Not yet another horizontal suite of products, which there are a lot. And that helped us quite a bit because when you focus on outcome, when you focus on strategic workflows that the companies need, it's way easier to attach yourself to the outcome the customer is looking for rather than be just a pure horizontal best-of-breed solution. So I mean, it's always important to see what's up there and where that dynamic is going. We can't predict 100%, but I think with 80-90% of probability more or less you can always see what's top of mind and what it would be for the next few years.

Right.

Correct.

Yeah, I'm looking at a very diverse set of data sets. So I'm looking at industry reports, for example, what's going on in the industry. What software is growing faster, what less, in what segments it is growing. I'm looking at what startups are growing faster versus those who are not growing faster. So you kind of meet a lot of founders, startups, and try to understand what are those type of startups, but also what's the business models. Some are PLG, some are sales-led. You can observe patterns in what specific niches the faster growth is happening. You obviously speak with your users and customers to understand how they do things today and where the biggest pain points are. So it's less about historical data and extrapolating that historical trend. It's more about trying to observe their behaviors and trying to combine that with the technological shifts that can unlock new behaviors. So that's an important one because now we are sitting on this major technological shift moment, and there are a lot of insights that we can bring from it to where the market would go. So what else? Yeah, reports from analysts and from strategy consultants, big one. Observing behaviors, understanding the technology shifts, and observing the startups and where the more pull is happening, those are things that I'm trying to constantly do and triangulate, and then you kind of make some insights and bets out of that.

Yeah, it's interesting because when I speak with other founders and CEOs, the common theme is that no one knows how the market will shape up. It's very hard to predict. And what I see and what we are doing is a portfolio of bets. So you put a portfolio of bets, you understand that not every bet will play out, but at least you kind of bet on things that most likely can be an accelerator for you. If you kind of eventually get to the point where every bet worked out, it means you were not pushing enough, you were not ambitious enough to put more bets that should fail. If everything fails, yeah, we'll see. It's also not clear how the market will shape, but at the end of the day, the bets we are making are aligned with our holistic strategy and mission. So our mission is to empower teams to create the next big thing, and whatever progresses that mission is our bet. So then obviously there are more things that we can potentially do than resources we have to execute. So we are trying to align the bets towards our strategy, what we're trying to achieve in the next two, three years. And we have several strategic bets in the company is how we align better to the product innovation leadership in the organizations and how we can help them accelerate the innovation velocity. And whatever works towards that mission and strategy, we put as a bet, and we put a team behind that bet, and we execute that bet. And then depends on the type of buyer, it's either PLG or is it SLG or is it a mixed model, but it all grounded into our core platform, so it's not a separate product, right? It's all grounded in the platform and it's all connected into existing behaviors that users and customers have on our core platform, and then we can amplify some of those products through internal platform distribution.

Yeah, it's definitely a major pull now by small businesses and users in the bigger companies to play with different products. We see it all the time. I'm not sure what percentage in Menlo Ventures' report is attributed to ChatGPT and Anthropic. I assume it's very big. I looked at the report they recently released, it was last week, and yeah, it's clearly saying 27%, which is incredible, but also the majority of spend in that category is OpenAI and Anthropic. So yeah.

Exactly. And both companies are PLG companies historically. So that's where I would be accurate in terms of extrapolating that insight to every product out there. So my assumption is the biggest portion of that 27% is just those couple companies. Gemini may be the third one, but they still kind of don't fully monetize those capabilities. So yeah, but then we definitely see examples of amazing companies like Granola, Figma, a bunch of other products that are distributed bottoms up, but their share is quite small now still. So we see quite an adoption for Miro bottoms up again across all segments. But what's important is when the customer is considering seriously AI technology beyond just this copilot or knowledge retrieval solutions, like again, OpenAI solutions and Anthropic solutions, they need to build a business case, they need to see why this solution is critical to be added to the mix in the enterprise. And you can't just rely on PLG, especially in the segment of midsize companies and enterprise companies. Even if you got some early adoption bottoms up, it doesn't mean the whole deal will happen because customers are looking at what's the strategic differentiation and why we need to bring yet another solution into our tool stack. So yeah, I think that's something to think through for those who are trying to build a PLG motion, and especially with AI, it's non-deterministic solutions. You need to not just kind of, hey, this works for a few people in your company, it's more about, hey, this is a strategic solution that we can go and implement and change the way how the organization operates, type of pitch. And this is very different from PLG.

Exactly. Why it's a strategic... I think PLG is a channel. It's not a business model, especially today. I think it's a great channel for people to explore and play with your product. But to be strategically deployed inside the organization, you need a different set of capabilities.

Yeah. And I mean, small deployments of 10k, 20k would work out fine, but if we're talking about more intentional deployments of 50k plus, 100k and above, it's all about building the capabilities inside your business to be able to build the business case and bring it intentionally to the enterprise.

Yeah. As you mentioned before, we're constantly iterating on our business model and our pricing. So we did so many experiments back in the day around this premium model to create the flywheel. We are now at the very beginning of this multi-product business model experimentation. There are things that we know and there are things that we don't know. The things that we know is that historically we have this per-seat pricing model, and with AI, this per-seat pricing model can be quite vulnerable because AI is more focused on outcomes, and we see that it's not equal distribution of those who are using AI capabilities inside enterprises. The companies may be shrinking, we will see more smaller companies over time than bigger companies. So you need to ensure that the business goes sustainably through those market shifts, and we are definitely experimenting how not to be just a per-seat pricing model company but go beyond that and build a sustainable pricing model that is having a consumption element but also that is having some other kind of more sustainable pillars.

Yeah. Maybe I'll start a bit again. So what we're looking at is how to diversify the business model for ourselves but also how to create a more sustainable and predictable business model for customers. So for us, just being a per-seat pricing model is quite a vulnerable position given all the shifts that are happening in the market. For customers, just being charged through consumption is also not the best way for predictability. So there are things that can work in the middle where you have more of the platform fee approaches which allows both to predict the consumption but also not be dependent on individual seats. Right. I think it will all evolve over time, but what we're trying to optimize for is for the customers to be kind of confident and can predict the spend with us as much as we and they can, and that's super important because it all builds trust. If you don't have trust with the customer, the rest will not happen. And while we are kind of building a business, we need to create strong levers for that business growth. In the first place, we need to ensure that our customers trust us and that we have good velocity of deals going with our customers. We don't want our pricing model to be a friction for the business. So we set up a set of experiments that we run. For example, for new products, we have the hypothesis that 80% of our team is running with, and then we have a couple more hypotheses that a subset of our team is running with, and that helps us to understand what works better obviously, what increases deal velocity, what drives higher ACV, what makes customers easier to commit to the proposal and what not. So it's like all those things, we don't know now how they will be eventually structured, but this experimentation mindset helps you to get signal as early as possible and not stuck with something that someone thought is a good idea but then three years later you realize that it's actually not healthy for customers and the business.

Correct.

And yeah, I think it's not obvious because pricing in every company sits somewhere, correct? It's just we have a very cross-functional team around pricing. I'm a part of that team actually because it's one of the biggest levers you have in the business. And I saw before what it can do if you do it right and what it can do if you do it wrong. And we have a team that has that experimentation mindset around pricing, and we have constant reviews. And we use Miroboard actually for the whole pricing history in the company. This is where it started, and every step of the evolution is mapped on that canvas, which is amazing because you always can go back and see what was that experiment, what was the outcome, why we did it, what we've learned and what not. So, and this allows you to kind of really always zoom out and see the bigger picture, but then zoom in into details and understand, okay, is it the best thing for us to experiment now versus what are other ideas to bring into the mix.

Correct.

I think, yeah. In a year or two, we will learn a lot about how our multi-product journey is going given that we just shipped our multi-product offering to the market just a month and a half ago. I'm very curious to see how the company will evolve.

Yeah, I think that in general AI is harder and will be harder to sell than just the previous wave of software. And the reason why is AI is a non-deterministic solution, and you can customize solutions for different needs, for different use cases, versus the previous wave of software was more like out-of-the-box software. And what it means for your motion is that you need people who have skills to go to the customer, understand their business process, and help them see how your product, like in our case it's Miro, helps transform that business process. So you work backwards from the customer needs. Sometimes customers will tell you what they want to achieve but they don't know how to achieve that. Sometimes customers don't even know the art of possible and what they want to achieve, and you have to come and see what's the opportunity there and help them see the art of possible and how it can be achieved.

Correct. And that gap is mostly what it... Exactly. And yeah, it's a great point because I just had a conversation last week with someone who is leading a company, and he was looking at our demo of the product and he was like, yeah, but how is it different from other LLMs that we can use for this use case and for that use case? And I had to spend a few minutes to explain the strategic difference. And once we got to this kind of aha moment, it was clear that we can potentially partner and go and explore deeper integration. But you need to bring people on the journey, and those people who are not playing with the products themselves may not realize the power of the products, and you need to bridge that. But on the other hand, end users, they may not see the whole big picture of the business process end-to-end. So they may see the immediate value for their specific area of use case, but they can't influence the broader things. So you need to bring those together and create those alliances inside the companies. But in general, the whole motion of selling AI products requires a very different skill set from selling just pure software products, and that's the transformation we are going through with our team, like trying to learn way more about the processes and the problems our customers have and working backwards from that.

Oh, I did it for the last almost a year now. Yeah, of course. Because now we're shifting to this new platform vision, and I'm in this founder sales...

I'm trying to understand the signal because we spoke at the beginning about this product market fit and working backwards from the market. You can read all these reports, you can talk with the customers about their problems, but then you need to bring the solution to them and you need to see if the solution resonates and then can you progress the deal. And that's what I was doing for quite some time now because I don't want just to be theoretical about, hey, I read in a report this is what people need and then I see no results in our funnel because we forgot to do something here and there. I'm trying to kind of model the whole thing for myself end to end and see what will work, what not, what are accelerators, what are de-accelerators. And I'm doing it together with the team. So I'm working with a lot of talented people in sales, in success, in solution engineering, in product management, in product marketing to kind of go to customers and learn together as much as we can. But you need that kind of level of conviction and vision at the beginning when the product is not ready, when the pitch deck is very, very thin.

Yeah, yeah, yeah. You kind of need that conviction of the founder to go and try to sell. And if that works, great, then you try to scale. But if that doesn't work, I think it will be hard for others to also solve.

I am having a lot of fun because that's the stage I love a lot when you have a lot of kind of ambiguity.

And we were spending way more time trying to become somebody else.

Exactly, exactly. And I think that this will be required more and more because the market dynamic with how fast kind of new products appear, new technology appear. So I think it's a big demand now for going back to basics and do a lot of zero to one.

Because the need of the heart today.

I was observing a bunch of other CEOs of quite big companies. Historically, they were sharing with me that they were sitting down with individuals, individual engineers, individual product managers and whatnot to build those new products. I was trying to manage things through the organization, and now I shifted quite a bit. So I'm working together with a few individuals on each of those kind of bets. Obviously, I'm not alone there. It's like myself with our chief product technology officer and the team that we arrange. But you don't need a big, big team to pull something up. You need a team that can create that first version together. And the faster that team moves, the more conviction that team has, the better. And right, sometimes we help with what can be strategic bets from the macro level and whatnot, but sometimes we're helping with removing obstacles inside the organization to move faster.

Very true. I think that is a very interesting shift and I think all of us are actually, a lot of us are having fun, right, going through that. Beautiful.

Now when the pandemic started, we were 200 people and we had around four offices, but the majority were in one office and was very in-office culture, very heavy in-office culture. 18 months later, I don't remember exactly, was seven or 10 offices, now it's 14 offices across the world, but it's been a lot of offices building. And we had 1,800 people. And the thing that I would definitely do different if I have to do it again, I would have more established ways of working in the company because when you are in-office culture, you have all people around and information is flowing and people are operating in kind of the same way. But when you scale it and hire 90% of your organization overnight, you need to ensure that whatever worked there will work for the rest of organization. And people are coming to organization from very different companies, from very different backgrounds. The big tech, which is also very different between each other, but then from small startups all over the world and from midsize companies and everyone comes with their ways of working and expectations. And we didn't have those ways of working being established and that way enforced.

Second, we didn't actually put a clear expectations on how we operate here and what does it mean to work in Miro. We had values that we were communicating to people, but those values were not grounded into what set of behaviors we expect and what type of culture it is. And those things were kind of in the air.

The same thing. Yeah. It's like, and I wish I realized how important it is and I wish I would be prepared for that scaling moment. But then getting out of pandemic, the micro environment changed quite a bit. So winning business becomes way harder for us and the ways of working, we still saw a lot of kind of things were not moving in the right direction or not moving at all. Just because there were no ways of working or no explicit set of behaviors that we expect people would show. For example, experimentation, we talked about that, right? Like some people would say, yeah, we are experimented here, and some people never experimented and they were kind of pushing certain things that they believe are the right things without like framing hypothesis. Like all those things are quite disruptive and especially if you have so many people working remote across all different locations. So what we did, we translated our values into a set of behaviors. We introduced a couple years ago ways of working, how we operate and what's that framework is. And now just a few weeks ago, I was resetting the expectations with the whole company what this company is for, why we're here and what we expect from everyone here, like starting with myself, with the leadership organization, but that is an expectation for everyone in the company how we're executing, how we're delivering. That it's not like a place where you can kind of just come and do the work. It's a place where we all come to win together. And if we come and win together, it requires a winning mindset. It requires an effort that we put into that. It will not come naturally. I also restated something that may be relevant for our conversation that yes, we had this incredible product market fit and some folks in the company thought that maybe we all earn it all. It's like, well, yeah, it's enough, we earn it all. We're established company. And I restated that no, we have to re-earn and re-earn it again and again because it's not constant. Product market fit is a moving target. And I'm trying to explain that to the organization that what we have today, it doesn't mean we will have it tomorrow and we have to win it again, we have to re-earn it again. And yeah, I think people in general receive it very well across the org. Those who are here for the winning, it resonated with them. And I just want to be transparent and honest with everyone is like what this company is about and what this company is not about.