Monday.com’s CRO on how AI is transforming the company

Casey George has some pressing items on his Monday.com board: bringing more big businesses to the platform, revamping the way his team sells with AI, and helping guide the company through a period of flux for workplace software.

The workflow platform’s chief revenue officer joined mid-2025, and his tenure has been marked with major changes: Last month, Monday laid off 20% of its workforce, or 630 employees, in a bid to refocus on AI, though George said his revenue team came out largely unscathed.

Like other SaaS companies, Monday recently moved to a hybrid pricing model that blends traditional per-seat pricing with AI usage credits. And its share price remains down significantly for the year, despite stronger-than-expected earnings this month.

We spoke with George about this period of change, how he’s moving Monday upmarket, and the future of workplace software.

This conversation has been edited for length and clarity.

Monday.com had a big reorg and round of layoffs recently. What does that mean for the revenue team, and how are you adjusting in the wake of that?

One thing I’ve always been impressed with is how we use our own product. And our product…is a great platform for productivity. It’s a great way to manage work, etc. And so, with the advent of AI…everyone in the organization is a super user. As a result, we were starting to see some significant efficiencies built into the organization, and because of that—and the fact that we knew that we needed to move faster in this new world—we decided to eliminate some roles. We did that primarily outside of the revenue organization, or better yet, outside of the quota-carrying organization. We left those roles, for the most part, untouched, with some exceptions.

What are some of the ways that the revenue team, in particular, is using AI, and what are some of the things that you’ve implemented in the 15 months that you’ve been there?

We did an Agentic Week, so we took the entire company, and we built agents and had a contest, and we had prizes, and we did a lot of pomp and circumstance around that…We built over 3,000 agents, and these agents—about 40% of them—are still in productive use. So as a result of that, we’re seeing a ton of productivity gains.

We have about 300,000 leads a year. Those leads sit in contact sales, sit in marketing, landing pages, all different signups, etc…There’s no way I could have enough XDRs [sales and business development representatives] to respond to all of those leads in a timely manner. So we stood up agents toward the end of last year, and now our agents consume about 50% of our leads. Our response time for SLA [service level agreement] has gone from 18 hours to two minutes. We have agents that actually, when a customer comes to us and says, “Hey, I want to speak to someone in sales,” our AI agents—[named] Amanda and Jax—they call the customer, they ask some qualifying questions, and then they schedule those meetings. They put all the details in the CRM [customer relationship manager], and so when the rep gets that opportunity, it’s now “an opportunity” instead of a lead. They have all the context from the customer, and we responded to them in a very quick way.

How do you determine which of the thousands of agents is useful and avoid agent sprawl with duplicative tools?

What we’ve incorporated is a center of excellence. Every department inside Monday has an AI leader, and that AI leader operates as, so to speak, a center of excellence to help us avoid things just like that.

Monday had a shift in its pricing model pretty recently. What was the old pricing model not capturing about this new AI era, and why did you change it?

We’re all victims of the token model. I don’t think anyone loves it, but it is what it is. It’s the best thing for where we are today with AI, and because of that, you have to accommodate that model as the underpinning of your pricing strategy. What we try to do, based on the feedback from our customers, is make it as simple as possible and be very transparent.

Customers seem to like it. I’ll say this as well: we have a customer advisory board that we lean on pretty heavily, and they were heavily involved in guiding us through this as well. So we had a pretty good affirming set of customers that we could bounce this off of.

What’s the pitch for businesses to use Monday’s agents and AI features rather than integrating their own agents with ChatGPT or Claude?

First of all, our platform allows for the collaboration and integration of agents that are outside of Monday. But when you build on our platform, whether that’s agents coming from another platform or another tool, or using agents on our platform, it includes all the context, governance, permissions, security, all the things you expect. That’s where we think we really have an advantage in the market.

As you move kind of upmarket and cater more toward bigger organizations, what has that meant for the revenue org?

That motion started previous to me. I was brought in to accelerate that, for the most part, and that has worked so far…I’ve only been here 15 months, but what I realized is that…in an organic way, [our platform] has been pulled upmarket.

A couple years ago, we got a little bit more deliberate about that by making the product more enterprise-ready, and that has paid off. You saw our results: record numbers in our $100k+ and $500k+ [annual recurring revenue] cohort of customers. The growth rate there is fantastic, and so that’s affirming that some of the bets we made to move upmarket is working. It is still early days there as well. It’s a very fertile ground.

In terms of accelerating that motion, as you say, what does that look like on your end?

It’s definitely a different sell. The talent that we have been recruiting, it’s different—they’re more enterprise sellers, less transactional. [They] understand a lot more business concepts, understand industries. We are aligning to where customers want us to be, which is, “Hey, come talk to me about how you’re going to specifically help me in my particular industry, in my particular department, in my particular domain.” And that requires a different seller.

It’s not to say we’re going to abandon the lower end of the market—we do quite well there, and we have a set of sellers that participate in that.

The other thing that they expect is to have more technical resources to be deployed. So we have grown our pre-sales and [software engineer] organization to accommodate that, and then the other thing that has happened in the last six months…is around our forward-deployed engineers. So with AI, customers are looking for help. That is very clear.

And this is where we see an opportunity, not only with the platform, but to be able to deploy the right resources and the ones that customers expect, so we’re leaning heavily on our forward-deployed engineer team.

The SaaS business model is in flux with AI. Looking to the future, how do you stay one step ahead of what businesses want, and how do you kind of see the SaaS business model changing?

Six months ago, SaaS was going to go away, right? What we saw for the past six months is, actually, SaaS is very well-positioned if you think about the technology layers. I joke with people and say, “That was a really expensive funeral, the last six months.”

But the reality is that the application layer is the one that’s going to be able to deliver that value. And I think LLMs become a commodity; it’s infrastructure, so to speak, and context is really the value driver inside your enterprise. So if you have your specific data with your context, with a platform that manages your workflows, that’s where the value sits. And to be able to integrate AI inside of that is super powerful.

I’m not naive to say that AI will not cannibalize some software. I do think that’s going to happen because you can do some pretty cool things, great things, in a very quick amount of time, for niche-type applications, stuff that doesn’t require a ton of integrations, doesn’t require a ton of data, connections, integrations…But those platforms like Monday and some of the other applications out there, they’re integrated into their business environment, and customers appreciate that. So being able to deploy AI inside of that is really what customers want to do. I hear it every day. They say, “Listen, we’re gonna deploy an AI strategy, but it’s gonna be inclusive of our current platforms we have today. We’re not interested in building another platform and bringing it in.”

Because [you’ve] got to secure it, maintain it, monitor it. [You’ve] got to make sure that it has all the permissions, integrations, etc. And [if] it doesn’t, it’s going to cost more.