monday.com Ltd. ($MNDY)

Welcome, everybody, and thank you for joining us. It's a great pleasure to be with Roy Mann, Co-Founder and Co-CEO of monday.com; and Eliran Glazer, Chief Financial Officer. Roy and Eliran, thank you for joining us. For those that aren't familiar, would you guys mind doing a quick introduction of yourself and let us know what problems monday.com is solving today?

Yes, sure. So Roy Mann, Founder, CEO of monday.com. So monday has been around a little, our CFO.

Yes.

And monday has around 250,000 paying customers. We have customers for across over 200 different business industries, and that's really everything and what they use us for is to manage workflow and really orchestrate their entire operation. So we have even manufacturing plants, people manage clinical trial research on monday. Manage all kinds of operations. One of our largest deployments is like 80,000 people. So that's like massive company managing their whole complex project management and everything around it across everyone. So it's like it can go into expensive depths and scale while also being super simple. So like everyone can pick it up, use it, manage their own stuff, and that's why we win because it's like really simple. People loves it. Employees love it. But you can also get into a massive depth. And recently, we changed the vision of the company from managing work, managing stuff to doing the work, which is do it with AI. And quite recently, we launched our Agent Platform. But before that, we also had like vibe coding on top of the platform, which has like been really successful and was our fastest adopted product. And other AI capabilities. And now we've completely changed the vision and the pricing and everything to go AI.

You hit on the exact topic that I was going to hit on next, which is you're transforming to an AI Work Platform, right? It's not work management with AI features, but redefinition of what your platform is and what it's supposed to be used for. Can you just in your mind, why now, why this magnitude of the rearchitecture? And what are you enabling customers to do that they couldn't do in kind of the previous paradigm that you had?

Yes. So like we call it sparkling AI on top of your product, that's what everyone has been doing so far. And unless you're like all-in in there, then it's very hard to also change the go-to-market to go after like the new kind of demand that is coming, okay? Like AI is going to be a way bigger market than software. In terms of spend and like the value it brings. And obviously, in the future, we have to go over it. So no sense of like not going all in. And all in means, basically, we also changed the pricing model. Like if -- until now it was seat-based, now it's also a consumption base, but -- and like new customers have to have AI in there. Why? Because this is who we are, like we have agents, it's the base of our stuff, but we enjoy the core offering of Monday of being massively scalable and easy to use orchestration system. So essentially, if in the future, everyone will have an agent or 2 or 10 doing work for them, then monday is the best place or we're building everything that is needed for people and agents to work together in large companies, okay? So you have auditing, you have monitoring, you can know that the work has been done, account for it. Also the pricing is super important and the accountability on the pricing and seeing that you can monitor and perform. So we're really taking the agentic world into what we really know well, large enterprises and collaborative areas.

Yes. You said that AI is going to be a way bigger market than software. There's a lot of people who think that if you look at some of the valuations of these private companies, and you might think that as well. But you've -- I think you say from a racial standpoint, you have $250,000 -- excuse me, 250,000 customers, right? What gives you confidence in that statement that AI is going to be such a bigger market. And then as a second piece, when you look at some of your most innovative customers, the ones who are leading in the most, how are they using? Because I imagine like you said you kind of have to go all in even from the customer perspective.

I think around 6 months ago, I started the monday [ agent labs ], which is like our initiatives to like be on the frontier of agents, like I built personally, like my background is development, so I'm back to that. And I built a whole agent harness and everything myself to kind of test it and we're testing stuff out and if you come and work with my teams right now in the lab, it looks like the Black Mirror. We have like really it's like a fun like you have a group of agents and people working together they have names. They have personalities like we know which one does what and how and whatever. And they also are part of the conversation, and they're doing a lot of lifting on anything. Okay, really. And like we built a lot of them on [ open cloud ], which is very cutting edge, very unstable and less reliable. And the idea of the labs is to take the -- first of all, to be in the cutting-edge environment and like to know what to expect, and I can give you some really cool examples of how is it that agents and people work together when it's like really working well. But then also translate it into what people can adopt because I don't think people can adopt open cloud, very unstable. It breaks all the time. It's like nonpredictable. Very hard to like harness to whatever you want to do. So that's the stuff we fixed and brought into the monday agents. So I can give you like one cool example of like story sort of. So in the teams, I work with, we develop a lot of like tourism experimental tools or whatever. So I had my agent in the group and someone else has had their agent, and I asked their agent to do something for me. And she said, like, okay, I can't because I don't have that API endpoint to do what you asked. And then my agent on the team who has access to the repository, told her like, hey, I can develop that for you. So she went on developed the API for that agent, gave her the link, used it and did what I asked. And then they were high-fiving each other in the group. Thank you. You're amazing. No, it's great that you're here or like whatever. Like it's -- so like I ask for 1 thing and then another agent listened in, not to what I asked for what she said and did another thing I didn't intend for like thought about and then they work together to solve the problem and come back to me with what I asked for. So I think this is kind of like a scenario that you would expect to happen for everyone. We're not yet there. People are not yet there. Expectations are not there. And it's also hard to harness and adopt, and that's what we built in monday, and that's the future kind of building towards that you'll have those environments where they can talk that they have the same information, the same infrastructure of connectivity to other platforms and API and everything, and it's out of the box. Like if you need to connect your agent to everything, you're not going to be able to do it. So think about the IT connecting your data system into monday, which -- it's already connected, okay? We have the APIs for everything. But now you can make it accessible for agents. So any employee building an agent boom, its possible to all the data that they need from their sales force from projects, on their financial system, HR system, anything, okay, and is able to do way more stuff out of the books. And I think those kind of stuff is like when you scale those solutions and make them accessible for everyone.

It was a great anecdote and you weren't joking about the Black Mirror analogy at all there. Hopefully, it ends better than those episodes -- I want to turn it over to [indiscernible], but 1 quick question for you, Eliran. Q2 guide for -- was at the midpoint of $355 million. I think it's about 1% sequential growth from Q1 to Q2 tighter than you guys have kind of historically guided creates a little bit of debate, if you're bearish, it's not as good as usual, if you're bullish, it's room for you guys to kind of beat and it's a conservative guide. How would you frame it? What do you think is the biggest swing factor in terms of like what the potential outcomes are?

So in terms of how to frame it, is last year, just as a reminder, we announced 2 years ago a pricing adjustment. It was, in effect, last year, contributed around $10 million in average per quarter. This year, we see less of this impact, obviously, because it's lapping at the end of Q2. So this is the main reason why Q2 versus Q1 on a consecutive basis, it's only 1%, but we also look at the year-over-year basis. In addition to that, the fact that we are -- the business model of monday, we called it out that we are seeing softness in the lower part of the business, the SMBs. But on the other hand, we see very strong momentum on the enterprise, but there is also a timing differences because when you have the SMBs and they swap a credit card, it's not that you get ARR and revenue immediately. But when you have it at an enterprise business, it's towards the back end of the quarter, and therefore, there is a timing with regards to when the revenue is being recognized from ARR. So these are the main 2 differences. What do you believe to be a potential strong momentum going into the rest of the year, and will drive potentially a positive news is continued success in the AI. AI in Q1 was 10% of net add at ARR was mainly driven by [ Vibe ], which was very successful. Monday AI Blocks and sidekick. And in addition to that, the strong momentum that we are seeing within enterprise customers expanding -- continue to expand definitely within the offering that we have.

Yes. Putting some of those AI products into the broader financial picture of the story. On the Q1 call, Roy, you disclosed that AI was approximately 10% of net new ARR, adding you to a smaller list of software companies that are very much made that AI contribution clear. Where are we on that AI adoption S curve inside the monday base? Is the typical buyer still experimenting with just a single agent? Or are we looking at production deployments across the broader portfolio?

So I think we're in early stages of those adoption even with Vibe, which is like a vastly popular product. We still see a lot of room to grow. And with agents coming out, like we were super positive towards the future and hoping it will be like even more faster adopting than other stuff. Basically, because the UI is super simple. To generate an agent, you just need to -- like if you want agent to, let's say, go over your e-mails and send you a weekly summary of something, just -- that's what you asked. Go from email, create an agent that go from my emails and whatever and then ask you like, you want me to connect your Gmail you'll click a button. That's it. It's done. So it's that simple. If you wanted to do any kind of other work, that's exactly how simple it is still to create one. And it's inherently connected to everything in monday. So if you wanted to automate like the stuff you have with us, which is the core work. We already have all the data you have of your operation, your inventory, those kind of stuff. So imagine what you can ask agents to do and automate those stuff. So I think that's where people will start and it's huge values even for SMBs, mid-market and enterprise, it's like -- I think it's going to bring massive value to customers.

I want to add maybe one thing. I think it's probably now we are about completing 12 months the first cycle of [indiscernible] companies, was soft companies. There was a former in the market. Everybody was trying to buy consumption-based and people bought a lot of AI and suddenly, the budgets are becoming very significant, and we need to manage the cost. So now experimenting, they would want to see immediate value. They want simplicity. They want to see how it helps them to do what they want to do from doing the work or from managing the work to doing the work. And I think we're coming at the right time on that front with the S curve. This is why I think it's the beginning, as Roy said, and now people or some of the customers are going to be more confident in what they want and we have their offering.

And what gates or changes the acceleration of that slope of adoption? Is it the product? Is it feature parity that they need to be there? Or do customers just not really know what they need yet or even how to build what they need.

Yes. So I feel like it's the last thing you said, like basically, everyone was minding their own business while AI came. It's not something that people say, oh, you really need it. And then we invented that technology. So a lot of them are, I think, being forced to kind of deal with it, okay? And if last year, everyone talked about it, this year, they have budget and our testing stuff. And a lot of them might still get like resistance from the teams, okay? Because they'll say, oh, I have an agent to do that. And I'll tell you, like it's not doing a good enough job, because I feel my job is at risk, okay? And I think next year, what I expect customers to be more forced into doing it. Why? Because if you're not, then you're behind professionally. This year, they can still make excuses. Okay. And on a positive way of saying it, they still don't really understand what they can do with it. So we see a lot of requests from us for customer success. For [ FDs ] like for [ engineers ] to actually come and help them do those transformation. So we do see a lot of positive approaches, but I think it's not that easy for them to adopt. I can share that when we released agents, we had agent week in monday inside monday, and we created over 5,000 different agents in 1 week with crazy stuff, and we're going to release all the examples and everything. So customers will have ideas of what they can expect and do. But people build amazing stuff that would otherwise would not have been done. Like going after all our like thousands of web pages and looking for like broken links and broken things like going and checking our brand across all our assets and that it's aligned, creating like a research on competitors and updating the battle cards for sales agents real time. Like there was news in the morning. It's in there, okay? So they're always up to date, that's something that's like inherently is not. And like I can go on for like so many different stuff we've built. So I think that inspiring -- that's going to be inspiring for a lot of customers to try stuff out and like really try and get the value out of it.

Yes. I mean the agentic opportunity here sort of seems incredible. Eliran on, from a modeling perspective, for people who are trying to figure out what that looks like within the monday business. Is it right to frame the AI contribution as addition on top of seat growth or seats? Or is this something that eventually sort of rebase the whole book? And maybe we remove a set or 2, but we add 10 or 20 agents on top, sort of walk us through how that looks?

Yes. Sure. So on the short term, definitely, it's an addition. So the pricing model for new customers, it's going to be comprised of feedback plus tokens. You have to buy credits in order for you to use monday. And we are agnostic on what AI product you are going to, if you have 1000 credits, use them the way more appropriate. This is also important for customers. I mentioned earlier that many of them that only looked at consumption suddenly lost track of budget. As a CFO, I see now the edge of automation, it's becoming meaningful, and I have to tell [indiscernible] develop less, I'm joking. But this is the idea. So I think in the short term, definitely a combination of the two. It will give customers predictability, visibility, some control on the budget. And as we continue to scale, we are going to onboard gradually the existing customers. that have fit. So yes, it's going to be a combination of the two. And potentially, on the longer term, it might be skewed towards consumption because some of the -- you will have the seats number may be going to be steady, but consumption and usage of AI will be more significant. But it will happen, I think, in the mid- to longer term rather than immediate term.

Right. Now that combination that you talked about is actually a great segue into the next question, which is on May 6, you announced something very interesting, the seats-plus-credits hybrid model. we are very much watching this, partly because if you can pull it off, it's a great example for what software companies should be doing to sort of help to fight against this "software" narrative going around right now. You framed the shift on the call, Roy, by saying, as AI takes on more work for our customers, our business grows with it. Can you walk us through the customer side mechanics here? How does the buyer decide what credit pack to commit to? What do true-up cycles look like? And I know it's extremely early here, but like what is early customer feedback on this addition or this new offer?

Yes. So I can share anecdotal stuff. It's like you said, really just launched like 2 weeks ago. So I wouldn't draw any lines into the future from this. But like we saw a few customers on the self-serve side that like have been with us for 5 years. And have not scaled to other products have not scaled in seats because they're like that size. And just now adopted agents and started like digitizing their whole company. And like -- so that's amazing to me because it looks like something people, specific people really want everyone wanting on the promise. Like I personally feel that like in -- throughout my years as a geek, I saw all sci-fi movies, and they promised us like AI sidekick, right, like the positive ones, right? You have Jarvis and Cortana and those kind of stuff. And I think we have that now. And I think people want that. It's not by accident that we have that in movies because you want that sidekick that does the work for you that you ask staff that is smarter than you, that is like more capable in some ways and, let's say, less more socially awkward in other ways and like has less act. But like that's exactly what they are. And it's amazing that we have that technology. And I think like people will find that easier to adopt and ask stuff. And those agents that we have today have to and essentially OpenClaw of like introduced that, and we took that and put it in our agents, which is 2 very fundamentally different qualities than what we had until now in AI, which is self-improving and self-reflecting. They -- you can ask the agent, what did you do yesterday, okay? What course did you answer? What like stuff you do you, and he'll know what it did and can summarize that, create reports on it, do whatever you want. But then you can say like, but yes, I think you did this call, and it's not good enough, okay, because you didn't do this and that. And you said, oh, okay. And look into its own code and see what it can improve and improve it. So it's self improving. So I think those 2 qualities did not exist like, let's say, 6 months ago. Where you had like agentic systems that are automatic and you need to. If you wanted to change the unit, you needed to go into the code and the architecture and figure out what the problem is and change it, and then monitor the quality or the output because the agents wouldn't know what it's doing on its own. So I think that -- those 2 stuff create like a completely different product offering that is way more easier for everyone to like adopt because you talk with the agents, like you talk with the person, you train them for the job, right? Like this is what you need to do to answer. You test it, like you ask [indiscernible] you see what it answered you say, why did you not know for pricing, let's say, if you want an agent to answer customer tickets. And he says, because I don't have the information on pricing. So, okay. So like go into that website whatever take it, and then you ask it again until you are happy. And then you say, okay, how can I connect you to the website that you answer customers? So like you work with the agent on the job that you're training the agent for.

Right. Continuing on this sort of sci-fi Black Mirror, Jarvis sort of topic. Agent talent. AI is something -- 1 of the more exciting things that you've launched this year. For those of you in the audience, I don't know, it's a managed marketplace where enterprises can effectively hire AI agents built on AWS, piping into Anthropic in similar models from the other vendors. I've stroke through it a couple of times and seeing an agent that can basically carry out a task for you that you would otherwise hire a human to do is incredibly interesting and sort of makes it feel a lot more real. So can you talk about where agent talent fits into the monday platform and how customers are adopting things like that?

Yes. So basically, agent Talend is something we launched out of the monday agent labs. So it's an experimental thing that we see across the whole system and not necessarily the platform specifically. What it does is like it's a marketplace and if we have these type of agents or self-reflective self-improving why not hire then? Because you can ask them for the job. So that's what we did. We -- and what is the easiest thing any company can do to adopt AI is open a job position, right? Like everyone knows how to do that. So we said, okay, you have no idea what AI is, you don't have to build your own agents, but you have a job for an agent listed with us. And then we opened it up for the whole, let's say, open source or agent building community, which a lot of people just like bought OpenClaw, another clause and whatever that they will apply for the role, and we have some positions already occupied to do social staff to do like checks and external stuff. And it's really cool to see companies who have less idea about the technology and having a lot of people with a lot of knowledge about the technology bridge the gap and connect together. So essentially we're not -- we built a whole screening process for agents and that we open source. So if you hire an agent to do your social media, you want to test them out and create a test and then like have the best agent pass the test, and pass, of course. So we kind of like give the test out for free as an open source where we want to build agents that like comply to that. and it's really cool. And what I expect is that people will also sign up for monday, build agents and be able to apply further jobs. So that's how we tie it into a whole ecosystem. But again, it's an experimental thing. It's like an idea into how we see the future of agents and people working together.

Anecdotally, I don't know if like, half related, but I felt today a tweet. And I think it's real that the big accounting firms, I did my training at KPMG, are looking for more AI efforts than CPAs or potential accountant, which is something that is speaking about talent and developing and everything. It's -- I don't know if I should be scared or worried or...

You're old enough to not be scared.

Turning to more of the sort of human side of the business, head count is a big topic in 2026 in this sort of genic world. How is monday thinking about heading into 2026, where are you adding people? Where are you holding flat, where you may be rethinking. Any sort of thoughts there.

So we said that we are going to be in the left earnings call, we said that we are going to be flat as of Q1, so around 3,200 employees by the end of the year. places where we're looking at. So we're very efficient on the G&A. We don't have -- if you look at the G&A percentage of revenue, it's 8% or 9% depending on the quarter. We're efficient there. So we would probably look at things like sales and marketing, still we are above 45% as a percentage of revenue, there are room for us to make some improvements there. For example, SDRs and BDRs rather than having people who are calling customers, we will have -- we already have agents that are doing it. We developed it internally. It's also related to some of the things that -- with the acquisition OneAI. We're looking at product. We're looking at engineering, marketing, these are the places. And obviously, we're looking at the business support function, but the bulk of the adjustment is required is going to be around the engineering and product. And what we are doing is because we also care a lot about the culture of monday is that in the past, automatically, whenever someone was let go or some left, we used to replace them. Now we're doing -- we're saying wait before we replace someone that just look what we can do and potentially have efficiencies with AI, but we do care about having people -- the people and the person in the loop of there. So it's going to be staged.

And we're also making massive changes in how the organization work. We're flattening the R&D team, breaking it out to smaller teams. They work faster and like on a lot of things. And because of it, we see a lot of efficiency there. And we're also having new roles now which people don't talk about. So like for the engineers, we didn't have until now, but customers really want them to help them drive adoption of AI and stuff. So that's like a new position that we scaling on, which is like awesome. So, yes.

Is there a tangible way to sort of quantify that return you're seeing on incorporating AI internally?

So it's a really good question because you see some people do what they do best being done like 100x faster, okay, like building a landing page. It's now instant and doing way more stuff and analyzing things and finding more problems we didn't before, which has saved a lot and you do a lot more. So yes, we definitely see it. And everyone like using agents or like [indiscernible] because it's like, wow, I'm like on a different level in my career. The question is, I feel like the real value is when you remove the person from the loop, meaning that like I'll give you an example, if we have a salesperson and it has a document with the customer that they need to go through like a legal doc. And you need a legal person to go over it. And now an agent goes over that doc and like red lines and stuff. If the legal person needs to approve it, then it's not really AI, right? Because like the salesperson still needs to wait. If it's completely automatic, then boom. It's like it changes the whole this dynamic of now I can do that on the call with you. Okay. Let's give it to the agent boom and like I got it that line back and like this is fine. This is not let's continue conversation and close it in 1 look. So I imagine the customer has the same range on their side. And they like you might a person in the middle at the agents talking closed the deal. So I think that those kind of stuff will start seeing soon. The legal team is in charge of like building the agent that does the red line rather than have the lawyer empowered by doing instead of like 2 hours work, like 5 seconds. So I think that's like -- it looks like [ 1,000 ] but it's not -- if it's in the loop and it's. So I think also for us, but also for other companies that's coming for a lot of like nondevelopment areas. In the R&D, we've had like massive changes.

Right. I know we're coming up on time, so I'm going to pass it off to Arti.

Yes. To close Roy and Eliran, if we're sitting here a year from now or the next conference, what is one thing that you think to the audience, welcome to appreciate that they might not at this current moment? What are you seeing that's going to surprise then?

I think the one thing that I think is everyone is talking about it, but it's super not clear is how much organization will resist the change, okay. Because you bring an agent that does something and no one wants to be replaced. And in developers, I saw that they're hesitant and then they like the technology and they go on top of it and use it. But I think in a lot of positions, they're not able to do it, a if you are adopting it and being replaced. And so I think we're going to see a lot of that. And it's interesting to see the future that we envision that like you want the productivity and you don't want the organization to resist. You want to talk them along with you to gain the like upside of like producing more, selling more and doing more business rather than slowing yourself down and saying, oh, that's not good enough, okay? So I think like there's going to be a lot of adoption challenges that the right product into solvent and also scalability and our ability to like monitor a lot of those kind of stuff rather than just the technology itself and how good or like qualities real-world problems in adopting AI.

Yes. Do you -- how does that friction gear resolved? How is how are people going to adopt this technology if it's going to replace them?

So I think unless you bring them along, they're not trying to resist as much as they can. I have a lot of stories for like in the past for like companies that would just like not do the change, like they saw the company's tanking without the change and still couldn't do it because like everyone resisted. So I think that's like you need to bring people along with you, and I think we're going to introduce a lot of -- and find good practices of how to do it. And I think going around saying as of like is your job secure why hire people, hire an agent. I think that's like not the right approach for anyone in this adoption of this technology, okay? You need to bring people along with you to make the change and like and show them that this is the future of their careers. And they need to make it, okay? It's like not everyone can make it. Like you need to adapt yourself as a person into this new technology. And being able to make the change. But I think employers need to give that option and pass for people.

Yes. Awesome. Thank you very much for your time and insight.

Thank you.

Thank you.