Welcome everybody. Thank you for joining us. It's a great pleasure to be here with Roy Mann, co-founder and Co-CEO of monday.com, and Eliran Glazer, chief financial officer. Roy, Eliran, thank you for joining us. For those that aren't familiar, you guys mind doing a quick introduction of yourselves and let us know what problems monday.com is solving today?
Yeah, sure. Roy Mann, founder, CEO of monday.com. monday has around Eliran Glazer, our CFO.
Yeah.
Okay.
I said.
Yeah. monday.com has around 250,000 paying customers. We have customers across over 200 different business industries, that's really everything. What they use us for is to manage workflow and really orchestrate their entire operation. We have like even manufacturing plants. People manage clinical trial research on monday.com, manage all kinds of operations. One of our largest deployments is like 80,000 people.
That's like massive company managing their whole complex project management and everything around it across everyone. It's like it can go into expansive depths and scale while also being super simple. 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 love it, employees love it, you can also get into a massive depth.
Recently we changed the vision of the company from managing work, managing stuff, to doing the work, which is doing it with AI. Quite recently we launched our agent platform. Before that we also had like a vibe coding on top of that platform which has like been really successful and was our fastest adopted product and other AI capabilities.
Now we've completely changed the vision and the pricing and everything to go AI.
You hit on the exact topic that I was gonna hit on next, which is, you know, you're transforming to an AI work platform, right? It's not work management with AI features, but a redefinition of what your platform is and what it's supposed to be used for. Can you just, you know, in your mind, why now? Why this magnitude of the re-architecture? What are you enabling customers to do that they couldn't do, you know, in kind of the previous paradigm that you had?
Yeah. like we call it, sparkling AI on top of your product.
Yeah.
That what everyone has been doing so far.
Yeah.
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?
Yeah.
Like AI is going to be a way bigger market than software-.
Yeah
In terms of spend and like the value it brings. Obviously it's the future. We have to go over it, so no sense of like, not going all in. All in means basically we also change the pricing model. Like if until now it was seat-based, now it's also, consumption, based, but, and like new customers have to, have AI in there. Why? Because this is who we are.
Yeah.
Like, we have agents, it's the base of all stuff, but we enjoy the core offering of monday, of being a massively scalable and easy-to-use orchestration system. Essentially, if in the future everyone will have an agent or two or ten 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. 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 and the pricing, and seeing that you can monitor and perform. We're really taking the agentic world into what we really know well, large enterprises and like collaborative areas.
Yeah. You said that AI is gonna be a way bigger market than software. I think there's a lot of people who think that. If you look at some of the valuations of these private, you know, LLM companies, and you might think that as well. You know, you I think you say it from a practical standpoint. You have 250,000 customers, right?
What gives you confidence that, you know, that statement that, you know, AI is gonna be such a bigger market? As a second piece, when you look at, you know, some of your most innovative customers, the ones who are leaning in the most, how are they using it? 'Cause I imagine, like you said, you kinda have to go all in even from the customer perspective.
Yeah. I think around six 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 build the 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 labs, it's, it looks like a Black Mirror.
We have like Really it's a, it's like funny. 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. They also are part of the conversation and they're doing a lot of lifting on anything. Okay. Really.
We build a lot of them on OpenCL, which is very cutting edge, very unstable, and less reliable. The idea of the labs is to take the, first of all, to be in the cutting edge environment and to know what to expect. I can give you some really cool examples of how is it that agents and people work together when it's really working well.
Also translate it into what people can adopt, 'cause I don't think people can adopt OpenCL. It's very unstable. It breaks all the time. It's non-predictable, very hard to harness to whatever you wanna do. That's the stuff we fixed and brought into the monday agents.
I can give you, like, 1 cool example of, like.
Go for it, please.
A story, sort of. In the teams I work with, we develop a lot of tools and experimental tools or whatever. I had my agent in the group, and someone else has had their agent, and I asked their agent to do something for me. She said, "Okay, I can't, 'cause I don't have that end, API endpoint, to do what you asked."
My agent on the team, who has access to the repository, told her, "Hey, I can develop that for you." She went on, developed the API for that agent, gave her the link. She used it and did what I asked her, and then they were high-fiving each other in the group. "Oh, thank you. You're amazing.
Oh, no, it's great that you were here," and, like, whatever.
These situations.
Like, it's whoa. Okay, like, I asked for one thing, and then another agent listened in, not to what I asked.
Yeah
She said, and did another thing I didn't intend for or, like, thought about.
Yeah
Worked together to solve the problem and come back to me with what I asked for. 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 we're 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.
Think about the IT connecting your data system in, into monday.com, which we, it's already connected. We have the APIs to everything. Now you can make it accessible for agents. Any employee building an agent, boom, it's accessible to all the data that they need from their Salesforce, from projects, from their financial system, HR system, anything.
Yeah.
Okay? It is able to do way more stuff out of the box, and I think those kind of stuff is, like, when you scale those solutions and make them accessible for everyone.
That was a great anecdote, and you weren't joking about the Black Mirror analogy at all there.
No.
Hopefully it ends better than those episodes usually end. I wanna turn it over to Jayden, but one quick question for you, Eliran Glazer. Q2 guide for, was at the midpoint of $355 million. I think that'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, you know, not as good as usual.
If you're bullish, it's, you know, room for you guys to kind of beat, and it's a good conservative guide. How would you frame it? What do you think is the biggest swing factor in terms of, like, you know, what the potential outcomes are?
In terms of how to frame it is last year, just as a reminder, we announced two years ago a pricing adjustment. It was in effect last year, contributed around $10 million average per quarter. This year, we see less of this impact, obviously.
Yeah
Because it's lapping at the end of Q2, so this is the main reason why Q2 versus Q1 on a consecutive basis is only 1%. On, we also look at the year-over-year basis. In addition to that, the fact that we are, the business model of monday.com, we called it out that we're seeing softness in the lower part of the business, the SMBs. On the other end, we see very strong momentum on the enterprise. There is also timing differences.
Yeah.
When you have the, you know, SMBs and they swap a credit card at no touch, you get the ARR and revenue immediately. When you have it as 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. These are the main two differences.
What we believe to be 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 added ARR, which was mainly driven by Vibe, which was very successful, monday AI blocks and Sidekick.
In addition to that, the strong momentum that we're seeing within enterprise customers, expanding, continue to expand definitely with the new offering that we have.
Yeah.
You know, putting some of those AI products into, you know, the broader financial picture of the story, you know, on the Q1 call, Roy, you disclosed that AI was approximately 10% of net new ARR.
Adding you know, to a smaller list of software companies that have very much made that AI contribution clear. You know, 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, you know, production deployments across the broader portfolio?
I think we're in the early stages of those adoptions. Even with Vibe, which is, like, a vastly popular product, we still see a lot of room to grow. With agents coming out, like, We're super positive towards the future and hoping it'll be, like, even more faster adopting than other stuff. Basically because the UI is super simple.
Sorry, to generate an agent, you just need to if you want an agent to, let's say, go over your emails and send you a weekly summary of something, that's what you ask. Go over my email, create an agent and go over my emails and whatever, it'll ask you, like, "Do you want me to connect your Gmail?" You'll click a button. That's it. It's done.
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. It's inherently connected to everything in monday.com. 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.
Imagine what you can ask agents to do and automate those stuff. I think that's where people will start, and it's huge values even for SMBs, mid-market enterprise. It's like, I think it's going to bring massive value to customers.
I want to add maybe one thing. I think this is probably now we are about completing 12 months. The first cycle of native AI companies versus SaaS companies, there was a FOMO in the market. Everybody was trying to buy, you know, consumption-based, and people bought a lot of AI, and suddenly the budgets are becoming very significant, and we need to manage the cost. 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. 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.
Now people or some of the customers are going to be more confident in what they want, and we have the right offering.
You know, what gates or changes the acceleration of that slope of adoption? Is it the product? Is it, you know, feature parity that then needs to be there? Do customers just not really know what they need yet or even how to build what they need?
Yeah. 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, I really need it," and then we invented that technology. A lot of them are, I think, being forced to kinda deal with it, okay? If last year everyone talked about it, this year they have budget and are testing stuff.
Right.
A lot of them might still get, like, resistance from the teams, okay. They'll say, "Oh, I have an agent to do that," and I'll tell you, like, "It's not doing good enough job.
Right
I feel my job is at risk." Okay? I think next year, what I expect customers to be more forced into doing it. Why? If you're not, then you're behind professionally.
Right.
This year they can still make excuses. On a positive way of saying it, they still don't really understand what they can do with it. We see a lot of requests from us for customer success, for FDEs, like, for deployed engineers to actually come and help them do those transformation. 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 an agent week in monday.com, inside monday.com, and we created over 5,000 different agents in one week with crazy stuff, and we're going to release all the examples and everything.
customers will have ideas of what they can expect and do. 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, research on competitors and updating the battle cards for sales agent real time. Like, there is a news in the morning, it's in there.
Okay? They're always up to date. That's something that, like, inherently is not. Like, I can go on for, like, so many different stuff we've built. I think that's 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.
Yeah. I mean, the, the agentic opportunity here sort of seems incredible. Eliran, from a, from a modeling perspective, you know, for people who are trying to figure out what that looks like, you know, within the monday.com business, is it right to frame the AI contribution as addition on top of seat growth or, or seats? Is this something that eventually, you know, sort of rebases the whole book, and maybe we remove one or two seats, but we add 10 or 20 agents on top? You know, sort of walk us through how that looks.
Yeah, sure. On the short term, definitely it's an addition. The pricing model for new customers, it's going to be comprised of seat-based plus tokens. You have to buy credits in order for you to use Monday. We are agnostic on what AI product you are going to use.
Credits, use them the way you deem appropriate. This is also important for customers. I mentioned earlier that many of them that only looked at the consumption suddenly lost track of budgets. As a CFO, I see now the usage of AI, you know, automation. It's becoming meaningful. I have to tell Roy Mann, you know, develop less.
I'm joking, this is the idea. I think in the short term, definitely a combination of the two. It will give customers predictability, visibility, some control on the budgets. As we continue to scale, we are going to on board gradually the existing customers that have seats. Yeah, it's going to be a combination of the two.
Potentially on the longer term, it might be skewed towards consumption because some of the, you know, you will have the seats number may be going to be steady, but, you know, consumption and usage of AI will be more significant. It will happen, I think, in the mid to longer term rather than immediate term.
Right. No, that combination that you talk about is actually, you know, a great segue into the next question, which is, you know, on May 6th, you announced something very interesting, this seats plus credits hybrid model. You know, 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, quote-unquote, you know, software narrative going around right now.
You know, 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 a buyer decide what credit pack to commit to? What do true-up cycles look like?
You know, I know it's extremely early here, but, like, what is early customer feedback on this edition or this new model?
Yeah. I can share anecdotal stuff. It's, like you said, really, just launched like two weeks ago.
Yep.
I wouldn't draw any lines into the future from this. Like, we saw a few customers on the self-serve side that, like, have been with us for five years and have not scaled to other products, have not scaled in seats 'cause they're, like, that size, and just now adopted agents and started, like, digitizing their whole company.
Like, So that's amazing to me 'cause, it looks that like something people, specific people really want. Everyone want in on the promise, you know? Like, I personally feel that, like in, throughout my years as a geek I saw all the sci-fi movies, and they promised us, like, AI sidekick, right? Like the positive ones. Right? You have Jarvis.
Yep
Cortana and those kind of stuff, and I think we have that now. I think people want that. It's not by accident that we have that in movies, 'cause you want that sidekick that does the work for you, that you ask stuff, that is smarter than you, that is like, you know, more capable in some ways and, let's say less, more socially awkward in other ways and, like, less has less tact. Like, that's exactly what they are, and it's amazing that we have that technology. I think, like, people will find that easier to adopt and ask stuff.
Those agents that we have today have two, and essentially OpenCL kind of like introduced that, and we took that and put it in our agents, which is two very fundamentally different qualities than what we had until now in AI, which is self-improving and self-reflecting. You can ask the agent, "What did you do yesterday?" Okay? "What calls did you answer? What, like, stuff did you do?" It'll know what it did and can summarize that, create reports on it, do whatever you want.
You can say like, "But yeah, I see you did this call, and it's not good enough, okay? Because you didn't do this and that." It says, "Oh, okay." Look into its own code and see what it can improve, and improve it. It's self-improving.
I think those two qualities did not exist like, let's say, six months ago, where you had like agentic systems that are automatic and you needed to If you wanted to change them, 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 agent wouldn't know what it's doing on its own.
Right.
I think that those two stuff create like a completely different product offering that is way more easier for everyone to, like, adopt. You talk with the agent like you talk with a person. You train them for the job, right? Like, "This is what you need to do to answer."
You test it. Like, you ask questions. You see what it answers. You say, "Why did you not know our pricing?" Let's say if you want an agent to answer customer tickets, and he says, "'Cause I don't have the information on pricing.
Right.
You say, "Okay. Like, go into that website, whatever, take it." Then you ask it again until you're happy, then you say, "Okay. How can I connect you to the website that you answer, customers?" Like, you work with the agent on the job that you're training the agent for.
Right. You know, continuing on this sort of sci-fi, Black Mirror, Jarvis-esque, you know, sort of topic, Agentalent.ai is something, you know, one of the more exciting things that you've launched this year. For those of you in the audience that don't know, it's a managed marketplace where enterprises can effectively hire AI agents built on AWS, you know, piping into Anthropic and similar models from the other vendors.
I've scrolled through it a couple times and, you know, 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. Can you talk about where Agentalent.ai, you know, fits into the Monday platform, and, you know, how customers are adopting things like that?
Yeah. Basically Agentalent.ai is something we launched out of the monday agent labs, so it's an experimental thing that we see across the whole ecosystem and not necessarily the platform specifically. What it does is, like, it's a marketplace, and if we have these type of agents that are self-reflective, self-improving, why not hire them? 'Cause you can ask them for the job. That's what we did.
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. We said, "Okay, if you have no idea what AI is, you don't want to build your own agents, but you have a job for an agent, list it with us.
We opened it up for the whole, you know, let's say, open source or agent building community, which a lot of people just bought OpenClaw and other Claudes and whatever, that they will apply for the role, and we have some positions already occupied to do social stuff, to do checks and external stuff.
You know, 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. We built a whole screening process for agents that we open sourced.
If you hire an agent to do your social media, you wanna test them out and create a test and then, like, have the best agent pass the test.
Right. Right.
Have scores. We kind of like give the test out for free as an open source for everyone to build agents that, like, comply to that.
It's really cool. What I expect is that people will also sign up for Monday, build agents, and be able to apply for those jobs. That's how we tie it into a whole ecosystem. Again, it's an experimental thing. It's like an idea into how we see the future of agents and people working together.
Right.
Anecdotally, I don't know if, like, half related, but I saw 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 experts than CPAs or potential accountants, which is something that is, you know.
Speaking about talent and developing and everything, it's I don't know if I should be scared or worried.
You're old enough to not be scared.
You know, turning to more of the sort of human side of the business, headcount is a big topic in 2026.
Yeah
In this you know, sort of agentic world. How is Monday thinking about heading into 2026? Were you adding people? Were you holding flat? Were you maybe rethinking? You know, any sort of thoughts there?
Yeah. We said that, in the last earning 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. We're very efficient on the G&A. We don't have, you know, if you look at the G&A's percentage of revenue, it's 8% or 9%, depending on the quarter. We're efficient there.
We would probably look at things like sales and marketing. Still, you know, 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 already have agents that are doing it. We developed it internally.
It's also related to some of the things that, you know, with acquisition of One AI. 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, if required, is going to be around S&M, engineering, and product.
What we're doing is, because we also care a lot about the culture of monday.com, is that, you know, in the past, automatically, whenever someone was let go or someone left, we used to replace him. Now we're saying, "Wait. Before we replace someone, let's just look what we can do, and potentially have, you know, efficiencies with AI." We do care about having a people, you know.
Yeah
The people and the person in the loop of the AI. It's going to be staged.
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 more things. Because of it, we see a lot of efficiency there. We're also having new roles, you know, which people don't talk about. Like, FDEs for each engineers, we didn't have until now, but customers really want them to help them drive adoption of AI and stuff. That's like a new position that we're scaling on, which is, like, awesome. Yeah.
Is there a tangible way to sort of quantify that, you know, return you're seeing on incorporating AI internally?
It's a really good question 'cause you see some people do what they do best, being done like 100x faster.
Right.
Okay? Like, building a landing page. It's now, like, instant.
Doing way more stuff and analyzing things and finding more problems we didn't before, which has saves a lot, and you do a lot more. Yeah, we definitely see it, and everyone, like, using agents are, like, ecstatic 'cause 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 an example.
If we have a salesperson, and he has a document with the customer that they need to go through, like a legal doc, and you need the legal person to go over it. Now an agent goes over the doc and, like, red lines it and stuff.
If the legal person needs to approve it, then it's not really AI. Why? 'Cause, like, the salesperson still needs to wait.
Right.
If it's completely automatic, then boom. It's like it changes the whole business dynamic, 'cause now, I can do that on the call with him.
Right.
Okay, let's give it to the agent." Boom, like I got a red line back and, like, "This is fine. This is not. Let's continue conversation and close it in one loop." Imagine the customer has the same agent on their side, and they like, you know, you might have a person in the middle, and you let the agents talk and close the deal, you know?
Right.
I think that those kind of stuff we'll start seeing soon, you know, where the legal team is in charge of, like, building the agent that does the redlining, rather than have the lawyer empowered by doing instead of, like, two hours work, like five seconds.
Right.
I think that's, like, it looks like 1,000x, but it's not.
Right.
If it's in the loop and it's. I think also for us, but also for other companies, that's coming for a lot of, like, non-development areas. In the R&D, we've had, like, massive changes.
Right.
Yeah.
I know we're coming up on time, so I'm gonna pass it back off to Artie.
Yeah. To close, Roy, I learned if we're sitting here a year from now at the next conference, what is one thing that you think the audience will have come to appreciate that they might not at this current moment? What are you seeing that's gonna surprise them?
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.
Yeah.
Okay? 'Cause you bring an agent that does something, no one wants to be replaced. In developers, I saw it that they're hesitant, they like the technology, they go on top of it and use it. I think in a lot of positions, they're not able to do it, okay? 'Cause if you're adopting it, you're being replaced. I think we're going to see a lot of that, it's interesting to see the future that we envision, that, like, you want the productivity, you don't want the organization to resist. You want to take them along with you.
Yeah
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? I think, like, there's going to be a lot of adoption challenges that the right product need to solve and also scalability and our ability to, like, monitor and a lot of those kind of stuff, rather than just the technology itself and how good or, like, cool it is. Real world problems in adopting AI.
Yeah. Do you How does that friction get resolved? How are people gonna adopt this technology if it's gonna replace them?
I think unless you bring them along, they're not.
Yeah.
They're trying to resist as much as they can. I have a lot of stories for, like, in the past for, like, companies that could just, like, not do the change.
Yeah.
Like, they saw the companies tanking without the change and still couldn't do it 'cause, like, everyone resisted. 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.
Yeah.
I think, going around seeing ads of, like, "Is your job secure?" "Why hire people? Hire an agent." I think that's, like, not the right approach for anyone.
No
In this adoption of this technology, okay? You need to bring the people along with you to make the change and, like, show them that this is the future of their careers. They need to make it, okay? It's, like, not everyone can make. Like, you need to adapt yourself as a person into this new technology and being able to make the change. I think employers need to give that option.
Yeah
path for people.
Yeah. Awesome. Great note to end on. Thank you very much for your time and insight.
Thank you.
Great.