How monday.com paused its roadmap for 30 days to hit AI escape velocity | Sergei Liakhovetsky

0:04Sergei Liakhovetsky:Today, I'm thrilled to welcome our guest, Sergei Liakhovetsky, the VP of R &D at Monday.com. Sergei, welcome to the show. Nice to be here. Thank you for hosting me. Of course, we're really excited to have you here. And today we're talking about how Sergei didn't just roll out AI to his team. Instead, he paused the rest of the roadmap for 30 days and pointed a 700-person technologist organization at AI enablement as the goal. And they came out the other side with something pretty rare. Every developer using AI daily, new platform capabilities, already seeing adoption numbers that most teams are not hitting.

0:43Sergei Liakhovetsky:And the numbers, they do speak for themselves. We're talking about Monday Magic with 5 ,000 solutions built in under three months, and Monday Vibe with 40 ,000 apps created in two months. Sidekick, 150 ,000 interactions in less than a quarter. And under the hood, you're talking about an insane acceleration of one of their biggest tech debt problems, cutting a 33-year investment down to just five months of work. And handling time for complex customer tissues dropped from three days to one, test coverage doubled, onboarding speed sped up to 21%. We're talking so many gains. So how do you build for humans and machines at the same time in this world and what happens when your organization hits AI escape velocity?

1:27Sergei Liakhovetsky:That's what we're going to find out today on Dev Interrupted. So Sergey, I want to start by talking about the numbers we just ran through. I talked about all the thousands of apps created by your users, the amount of tech depth that you've eliminated, and every developer using AI daily. And none of that happens without a lot of trust and reliability underneath. And when we talked initially before this call, you said something really stuck with me about trust being the currency for all of this. And this is the foundation that lets you ship all of these amazing features for your users. So when you look at monday.com's journey from B2C to enterprise scale, what were the first cracks that showed that told you, oh, well, we need to rebuild this foundation to get ready for this new era.

2:14Andrew:Yeah, so it's a great question, Andrew. And I think that we need to start, first of all, from the culture of Monday. Monday is a great company where we're focusing a lot on the customer experience. And when we're focusing on customer experience, we are looking at how actually our users will use the system. And this was the main driver of whatever we did so far. So first of all, the UX, first of all, the experience, and after it, look on how the system should work. So at some point, when we continued working upmarket, actually we saw that the system is lagging behind from performance perspective and from scale perspective.

3:01Andrew:And this is where we started looking on the different solutions, what we can do in a different way. And this is how the MondayDB, the first version of MondayDB, right now we're already in a third version of MondayDB, raised. We started looking on how we're building absolutely different way, how we're dealing with data. So from one side, we had a trade-off, okay, because when you want to have a great performance, you need to think how the user experience will look like. And this is where we decided that we initiated this project of MondayDB, and this project ran for two and a half years till we had the first release.

3:45Andrew:Actually, we replaced the entire underlying technology with the data management system that we built. We're using different foundations. We started from SQL, moved to Cassandra. Now we're using also in-cache databases like DuckDB and others, and absolutely different. So we started when the system was supposed to deal with the boards, boards, you know, in Monday, it's like tables. Right. Okay. That is keeping a thousand of items. And now we can maintain millions of items in one board, in one entity. And this is huge. So first of all, this was the first driver of moving forward with the foundation to support our customers to gain their trust, especially when we're talking about enterprises, about large customers that are looking for predictive solutions.

4:41Andrew:They are looking for the solution that will work with greater reliability, with the great availability. And this is what we did the first. And the second one that we're doing right now is the cell architecture. In the cell architecture, we are focusing on reducing the blast radius. How we're going to reduce the blast radius of incidence. So in a way, that one noisy account will not take the entire system down. All that together gives a lot of trust to our customers. And this is what we're pushing ahead. Because for us, customer experience is trustworthy customer experience.

5:24Sergei Liakhovetsky:So it starts at the beginning by understanding that you had to fundamentally improve your underlying infrastructure and architecture to handle the scale and the complexity needed by those customers. And that's nothing glamorous. That's like getting into plumbing and fixing things and ripping things out and making it better performance-wise with databases. This is before any kind of glamorous AI work.

5:47Andrew:Yes, absolutely. So if you're looking on the Monday journey, I think it's a bit different journey from other companies, other startups. First of all, Monday started from user experience. and after it moved to deal with performance and scale for larger customers. And after it, we moved to AI. So we're always looking for how to improve the experience for our customers.

6:14Sergei Liakhovetsky:How do you build this culture where investing in those foundations to make that great customer experience is seen as like a first-class thing to go after? And it's not just slowing you down. How do you align the culture around that?

6:27Andrew:yeah look when we're looking on the foundations first of all we're looking at like for me foundation is equal to standards so if we are going to our bigger customers that we want to gain the trust and we talk the trust in the currency for our customers and trustworth experience we need to make sure that whatever we're doing whatever we're building We're building like highways. Okay? And highways, to build highways, it takes time. But in many cases, when you're building this highway, have unlimited speed later on. And this is exactly our approach here. When we're going to provide the experience and where we need scale, where we need performance, we're building those highways.

7:16Andrew:We're building the robust foundation. We're using cell architecture. We are building our foundations, our resilient data layers, our additional shielding tools that are shielding and preventing incidents and providing resilient behavior. But again, it's not always the case. For example, right now, when we're talking about AI, it's not enough. We need to know how to balance between robust foundations and moving fast. So we are working as the builders all together to identify where we need to move faster, where we need less resilience and less scale, but we need to have speed. And we're building those products.

7:59Andrew:So experimenting from one side and on the other hand, to have the robust foundation for the solutions that should run in scale and run with the high performance.

8:12Sergei Liakhovetsky:Yeah, that's a really sharp observation and something we're going to talk about more a little bit later, too. The idea that, you know, you have to get the foundations together in order to go fast. And the highway is a promise for speed in the future. That's a cool thing about a highway. You can't build a highway in secret. Everyone knows the highway is coming and you're going to go fast in the future. So in the same way, you get everyone aligned around the same goal. We're going to make our foundation amazing so it sings. That way we can go really fast because we are in this like difficult environment.

8:39Sergei Liakhovetsky:environment. Engineering leaders every day are having to make tough decisions between, do I double down on our infrastructure and spend more time fixing our technical debt? Or do we just innovate at the speed of light and just hope that we just catch something that yanks us forward, you know? So it's like a real, it's a real balance, I think. And in this world, there's lots of metrics you can use to navigate the success of fixing your underlying foundations and uptime and data integrity. Those are just, you know, those are pretty straightforward. But what other signals do you or monday.com look for to understand how trust is increasing or eroding with your customers?

9:17Andrew:Oh, it's a lot of different metrics. It's not only about uptime. It's also about performance. I think that in a new world, especially when AI, when we're looking for, or customers basically are looking for a different latency and they are looking for instant responses from the system. performance is like if we have the high latency, it's like a download. We cannot, sorry, it's like an outage. We cannot afford it. Right. From one side. So performance is one of them, definitely. And we are defining the core flows and we're looking on each and every core flow to see upload, download metrics, and also the single interaction metrics.

10:05Andrew:So we're standardizing all of that. We're looking exactly every day, every minute, what's going on in the system. On the other side, it's also soft metrics like the sentiment of the customers. So we're running the service with our customers to get their sentiment. And when we see the sentiment is going up or down, we're analyzing it. We're trying to figure out what's going on. And we're moving to the metrics in the system to correlate between the sentiment that we are getting to what we see in the system.

10:39Sergei Liakhovetsky:That's very cool. So there's both trailing and leading indicators that you're looking at around this experience.

10:46Andrew:Exactly. And it's always balanced between both of them to see how we have leading the metrics like the sentiment of the customers and freely metrics to understand what is happening in the system.

11:04Sergei Liakhovetsky:So we've talked a bit about how you create this environment of trust and how you make the customer experience first class. from the very beginning of Monday.com to still now it's scale and size as enterprise-facing business. And now that we've kind of talked about maybe some of the less glamorous work of working on the foundation, there was a pivotal time within Monday.com that you explained to me that was really fascinating that I want to talk about before we move into the different kinds of AI offerings that y 'all have built out of that experience. And this is your 30-day AI month. And it can be pretty hard to convince an entire organization to pause a roadmap for 30 days, especially at the size of Monday.

11:43Sergei Liakhovetsky:But you pointed all of your technologists at one goal, you know, becoming an AI-enabled org. And in this month, you experimented with a lot of things with your team that I would really be curious to know more about. One of them being, how do you design a month like that so people don't just build demo after demo after demo, but they're walking away with durable skills that are going to fundamentally change how they work?

12:05Andrew:So thank you for your question because it's really interesting and I think it will be great exercise also for other companies and other leaders in other companies. Because, you know, before we moved or even thought about AI minds, it was like we worked with AI. We looked at it like features, but it was like a table stakes. You have features. Some teams are developing it, but it's not something that is transformational for anyone, not internally and not externally. And with the leadership, myself, Leron, who's another VP R &D, also products, started thinking about it, what we can do differently, how we can engage the entire builder's company.

12:55Andrew:It's like a company. We have about 700 people in builders. So 700 engineers we need to engage. We need to inspire. And we need to give them some tools for experimenting. hands-on experimenting. Otherwise, if it's only education and training, it's nothing for such a huge company like we have in Monday here. So we started thinking about it, and we defined to ourselves several principles. So the first principle was, we are not going to run it like a hackathon. Because when we're talking about hackathon, and hackathon is a very important vehicle or mechanism to reach the inspiration, to innovate. But in our case, we wanted to achieve something else.

13:51Andrew:We wanted to make sure that whatever we're doing is reaching production. That we understand not only what to do, but also how to do that. So the first principle was whatever we're doing is going to production. We're not going to do something only for the sake of experimenting here. The second one was what are we going to do with our commitments? We have customers' commitments. We have soft commitments. We have hard commitments. What are we going to do about that? And we started working with the teams. And just to give you some sense of the period of time, it was about two weeks work. Okay. From the point we decided that we wanted to have a high month to the point we started, we kicked it off.

14:41Andrew:It was only two weeks. Wow. And I think it's super important. And this is one of the main takeaways for me, at least, like lessons learned. if we decide to do anything, you need to do it right now. Even if it was some mistakes, if some mistake will happen after, we will change and we will fine tune as we are going. So the second point was to go through all the teams, and we have dozens of teams in Monday, and to make sure that we know how to deal with each and every commitment that we have to our customers. And at some point, we figured out that most of the work that we are going to do, we can do also with AI.

15:32Andrew:It's not like we need to stop everything and to start doing some esoteric things only for the sake of learning here. right so this was the next principle that we defined to our teams and to ourselves that whatever we are doing we need to make sure that it accelerates in our roadmap even if we need to reprioritize but at some point it should be fitting the product that we are doing and again at some points we saw that it doesn't fit and that's fine but those were like exceptions here this is how we started working this is how kicked it this is how we designed it and after it it was a huge amount of work to work with the teams and to run demos like you said and to have education and we had the champions program in parallel to that to make sure that people have communication channels and know how to get the data and to get the knowledge they are looking for it was from one side it was a huge amount of work on the other side it was so inspiring that people started actually fighting for going and showing the presentations and showing the demos of whatever they're building and this was great this is how we built the new products like you mentioned before, Monday Magic, this is where they started, Monday Vibe, and also Sidekick, the copilot that we're using in this system.

17:14Andrew:And also internal projects like Morphix for splitting the monolid, and Sharelock, where we succeeded to reduce the amount of time we're spending on tickets resolution by half more or less so if you give to people ability to fly they're flying just to give them ownership and give them to run i think this is the main takeaway from my

17:42Sergei Liakhovetsky:from the cci months i think that's a great lesson to learn from it and i think that's it really hits at the heart of why developers are developers like we're software engineers because we want to build cool stuff because every day we want to go to work and build something interesting that changes lives that, you know, is intriguing, but also just makes the world better. Like we're curious and we want to tinker. So the idea of having the AI month and aligning it around some ground rules, I want to run through them because they're really smart. One of them being that anything that you put together, it's not just throwaway hackathon.

18:14Sergei Liakhovetsky:Everything has to be aligned towards a business purpose, a goal. What was going to be worked on should have the intention of being taken to production. But then taking it one step further and working with all of the teams on an individual basis and understanding their needs and commitments to customers. And then you actually get an ability to map that into your experimentations with AI and what you're going to build and roll with. So now you're not just throwing like AI at the org and saying, figure it out. Let's learn how to roll with this. Let's see what we can make. Instead, you're creating like an art of the possible.

18:46Sergei Liakhovetsky:You're showing a space where people can come together and ship and show best versions of what they think the product could be. And I think that's the most exciting version of AI months that can happen inside of any company. Is you get somebody who can take the core idea, the market position of what you provide to your customers and take it to that next level with AI as like a concept. It helps align everybody, right, around the idea. Because right now we don't know what we're looking for yet. So I like how you kind of used the carrot on the stick, so to speak, like the guide, the experimentation towards what the customers and stuff would ultimately benefit from.

19:25Andrew:Yeah, I think that, you know, people need some space to experiment when they are looking for the mindship, the mental mindship. And what we're going through with AI right now requires absolute mindship in the way you're working, in the way you're looking at the staff, in the way you're practicing, and in the way you're experimenting. it. If you will not be able to experiment, if you will not give teams to experiment, it will be a failure. It will be like everything. And once you provide this ability, this tools to people to fly, they are doing miracles. It was amazing to see all the speakers, all the demos.

20:12Andrew:You know, overall, we had allowed around 17 workshops only during this AMI month. 17 workshops. We had about 22 speakers. And we had, yeah, and we had about 70 demos or something like that.

20:28Sergei Liakhovetsky:So just a huge number of participation. Everyone's really excited about figuring out what we can do with this and where we can take it. Yes, and people really loved it. In this world where they're like using all these different tools, you know, are you just kind of letting them experiment and grab whatever they want. You can use this tool, you can use that. How do you start to keep tools in check and understand what is our tool library going to be emerging from this month?

20:51Andrew:Yeah, so first of all, I think, again, it's a great question here because we started working with one tool, actually with Cursor, and we figured out that limiting people is limiting their imagination and their ability to move fast and to experiment. So we define absolutely different methodology and different processes here. In a way, we're working with tools, in a way, we're acquiring tools, in a way, we're experimenting. So we absolutely remove the barriers. Actually, personally, myself worked with security procurement and legal to define exactly how we're working from the one side to make sure that from security perspective, we are not putting our customers in threat.

21:44Andrew:So we worked with DemiData, for example. On the other side, we had zero bureaucracy policy here. And zero bureaucracy policy actually to make sure that in one week you are getting all the tools you want to get, but you need to be champion of the tool if you are asking for this tool. you need to make sure that there is no PII threat there and if after two months the tool is not used we're just removing it for our catalog. So you just

22:16Sergei Liakhovetsky:basically kind of just like open the door, let anyone bring the tool forward that they need to get their best job done. And then trade away is that I love how you smiled when you said, you know, it's no bureaucracy organization. You own that tool, which I think is easier it's pretty easy to do if it's a tool you're really passionate about you want to use. Like, oh, I don't want to be stuck using Cursor. I want to use this other tool. You're more likely to get a good champion who can help others than to learn how to use and get the most out of the tool.

22:47Andrew:Exactly. They made an amazing effect. Because people felt that they are owners and they wanted to make sure that others are using those tools. So it was a lot of communication around it. It was a lot of talks. It was a lot of smiles there.

23:05Sergei Liakhovetsky:Oh, I love that. Because then it's like, oh, this is my tool. I want to use this tool. So if I want to keep this tool, I should convince everybody. Yeah, look how it's amazing. Yeah, exactly. Look at my demo. Look at what I built. You know, I shipped this with this tool. This is the future. So what I love about this month you're describing is it's the right blend of incentives. You have the experimentation. You have the career growth. You have the alignment towards where the company is going to go next. You're tapping into all of that excitement. But what I love that you just mentioned, too, is that you also worked with security to keep things safe, that make sure there are boundaries.

23:37Sergei Liakhovetsky:You know, what did that look like just from like a bird's eye view of just making sure you had the right fences up for that month?

23:45Andrew:So, first of all, we worked with the app security team. So application security team was part of the committee where we decided what we can do, what we cannot do, how to shape and design the MCP, for example, when we're working with the tools. They were really engaged. Like, you know, not every app security team is really engaged. In this case, it was amazing. Amazing effect. They worked side by side with developers. They're like real builders.