Manhattan Associates, Inc. (MANH) Earnings Call Transcript & Summary

Good morning, everyone. We're going to go ahead and get started. My name is Brian Peterson. I'm one of the application software analyst here at Raymond James. Very happy to have Manhattan Associates with us today. We actually have a broader team here CEO, Eric Clark -- CT, I want get this right, CEO, Sanjeev Siotia; Michael Bauer from Investor Relations, Dennis Story and Linda Pinne. So thank you for bringing the Rockstar team here.

So Eric, we'll start with you, maybe kind of a 60-second overview for people in the audience that aren't familiar with Manhattan.

Great. Well, thanks for having us, first of all. And so Manhattan has been in business for about 36 years in the supply chain space. So if you break that down, where we really focus is warehouse management, transportation management, order management, point-of-sale and supply chain planning. Very well known in the warehouse space. What I always say is there's not a deal that happens in the warehouse space that we're not invited to. We've had a presence there and we've been recognized as a leader for many, many years. And then across those other products that I mentioned, we've entered those markets at various times. We are well known in some of those spaces, but not all of those spaces and big focus this year from a sales and marketing perspective is to make sure that we do get invited to every deal that we want to be in across all of those products.

And so you've been here for -- been in the seat for about a year. I'd love to understand maybe lessons learned and what are some of the key things that you've implemented in your first year in the seat.

Yes. So what I joined, I was, first of all, really impressed with the quality of the products. So I mentioned 36 years, but really, we're a 10-, 12-year-old cloud company. So this company has successfully reinvented itself a couple of times and most recently was this reinvention around cloud technology. And the reason it's so impressive is because Sanjeev and the team really committed to this cloud-first, all APIs, all microservice driven. And through the years, they haven't done things like so many other software players have, where they've bought things and bolted them on and integrated and so forth. And because they really stuck to that foundation, now that AI has come into play, it's made it very quick and easy for us to deploy AI natively in our platform.

And so I wanted to hit on both of that, but maybe just quickly, the fourth quarter that you just put up was very impressive, any kind of key highlights or anything that you call out on the demand environment for what you saw at least through December.

Yes. So you think about last year, Liberation Day back in April and all the things that were changing with tariffs and everything else, I think a lot of people were concerned in the supply chain space. Is that going to be a tough go. We saw the demand continue to be strong. You mentioned Q4 was a record bookings quarter for us. 75% of that came from new logo. And then full year, we had 55% of our bookings from new logos. So we're seeing a lot of strength in companies in the market that are truly going out and recompeting their solution. And when they are doing that, they're looking at Manhattan as really the only player that offers this unified platform across everything they're looking for.

It's all right. A lot of time on this. But just broadly speaking, I think there's a lot of concerns that AI will eat into the value propositions of App software. I know you both have written on this separately. So maybe I'll just open it up to you guys. What is your stance on that? And how should investors be thinking about the AI application for Manhattan.

Yes. So I'll kick off, and then I'll let our CTO and Sanjeev talk more about that. But yes, this is one of those narratives that I think got overplayed very quickly in lots of different ways. But the reality is there will be software and SaaS companies that AI can really eat at them, right? And I talked about it in the blog that I wrote as Winners and Losers. But the AI -- the SaaS companies that will benefit from AI, where AI makes them stronger, we'll have the opportunity to put that AI growth on top of the cloud growth. And that's the opportunity that we see. And maybe I'll let Sanjeev kind of dig into why we see that and why we're so confident that that's where we're going to sit in this.

Yes. And I think I wrote part of it in my blog too, right? So if you have a pharma database, it's easy to kind of like really think about replacing even though that's not that easy. When you think about a complex applications like supply chain and everything else, there's a lot more which kind of goes in. So I do not see a scenario where somebody -- we can would kind of go right this application up and somebody will just go buy that stuff, right? Now AI, this is a net additive to us, in terms of what we've done with AI and how we have done it with AI because AI is kind of natively built into the product. I mentioned in my blog, we generate a lot of our code. And if I go back to why we did all of those things that goes back to 14 years back when we started building this thing. First thing, when we said we had 2 choices, go, lift and shift what we had and put it on the cloud or rethink on how the world will be 5 years, 10 years from now and how do you prepare for the future. We made a decision that we will make a big investment to build for the future, right? And part of that, the 3 or 4 core elements went into it, right? What we saw generally from a past history perspective, right, is user interfaces change every 10 to 15 years. And when we we're building in 2014, we kind of knew that some sort of a user interface will come in the next 3, 4, 5 years, and we have to prepare for it. So we took an approach that we're going to build everything as an API-first approach, right? To be ready for whatever the new interface will be. We didn't know it will be AI, but now it's kind of proving that our strategy kind of worked really well from that perspective. Second thing as we look through this, right, is when you think about workflows, we serve all tiers of customers. And our premise was we're going to build everything as composable, right? We want to make sure that the our APIs, the workflows can be composed naturally, right, by configurations, by whatever else. And that, again, I'll come back and tie back to the AI, how it plays. So build it as a composable thing. The third piece, when you build enterprise software, right, no matter what you do, it takes you N number of years. It doesn't get done in a month, 2 months. And typically, when you build software, what happens is what you build in first month looks quite different than what you build in the fourth year, right? And then you build that technical debt all along. So we made a very strategic decision way back in 2015 that we're actually not going to write a lot of software. We're going to generate a lot of software, mainly from a consistency perspective, right, so that we don't accrue any technical debt as we progress. Those 3 aspects have really, really paid us well. We actually even begin with when people say microservices, when we started it off, I don't even call it microservices. I used to call it components or component types, right? And the microservices kind of word came in later on. And I feel like AI native, we are way ahead of that thing is if I think about building an AI native platform right now, I would do exactly what we did in 2014. So I would say we have been building this AI native platform now for the last 10 years.

Well, maybe just because I'll address it specifically. I think there's some belief that a lot of these customers may go out and build these things themselves. And I know you are one of the most -- WMS solution is very, very, very complex. But can you like address that specifically and why we wouldn't see customers trying to build these applications themselves?

Yes. I'll give you an example, right? So when we started in 2015, we already had a 20-year history with building WMS system. So we had all the domain experts in the house. We had a generator in 6 months, we wrote, right, which an AI could kind of write the generator and do something with it. And even with all that, it took a whole lot of us for 3 years to go build a system. So if anybody thinks they can go build WMS in a weekend, they probably do not understand the complexity of what is required, right? We have roughly, let's say, 180 components. So the magnitude of just understanding what to build is pretty large for somebody to kind of cross that bridge. So that's kind of one of the reasons why I feel that there's a pretty large entry barrier for somebody to go just go build a new WM system on their own.

So maybe I'll add to that a little bit. But what about the complexity to implement some of these systems, right? And then I guess maybe taking the question a little bit differently. What is the opportunity for AI on the services side to potentially get more efficient? Is there anything that you can do there with customers?

Absolutely, right? I think Eric talked about it a lot from a simplicity perspective, right? I think the time to value to accelerate is absolutely exists, right? We are looking at every aspect of implementation, whether it's interface mapping, design, how do you convert configurations, how do you test, all of those aspects, AI will absolutely be 100% effective, right? And we're seeing time reduced, right? We're seeing acceleration from our implementation perspective with respect to AI.

And part of what that has allowed us to do is as we leverage more AI and automation in our services business, it's allowed us to start to do fixed price and fixed time line deployments. Now we've started that with our conversions because we know, hey, you're running on one of our on-prem products. We know exactly what you're running. We know exactly where you're going. So it's very clear to us what that's going to take. And so we started offering fixed price, fixed time line on that. But I expect that over time, as we get more and more confident in some of the automation that we're creating that we'll do that with -- we'll be able to do that with new logo eventually also. We also have the opportunity even more quickly to go and do that with customers that maybe have licensed software for, I don't know, call it, 50 warehouses, but they've only deployed 20. So we want to go help them get the next 30 deployed more quickly. So these are all opportunities to leverage that AI and automation to move faster. Because if you remember, our -- the way our revenue ramps, it's based on a contracted revenue ramp, we can drive them faster than that, revenue ramps faster.

Maybe just to remind us, where are we on the conversion time? I know most of the WMS space is still on-premise today, but where are we in that conversion effort? And like how should we be thinking about that trajectory over time?

Yes. So what we shared on the last earnings call is that we've got about now 22% of our on-prem customers have either started or completed that journey to the cloud. So we still got a big, big opportunity for conversion. And we've had these cloud products now for, call it, roughly 10 years in most cases. So only 22% went in the first 10 years. I think in the next 10 years, we'll see it go a lot faster than that. And part of it is it's just natural. A lot of these companies forever have been on a cycle where they do an upgrade every 5 to 10 years. So some of them just weren't on their cycle yet. But now we've got this case where the gap between what they have and what they could have is just getting bigger, faster. And we're never going to build AI into the on-prem platform. So to take of all these new features, they've got to get to the cloud.

And maybe just like thinking about WMS specifically in terms of the competitive landscape, what are you seeing out there? And is AI increasingly more important in these conversations? Or is it not yet? Or is it on the road map? Or I'd love to get a competitive update on WMS.

Yes. So from a competitive update standpoint, we mentioned we still have 70-plus percent win rates. I also mentioned in the last earnings call, a lot of people consider Blue Yonder to be our biggest competitor, and we've got 90-plus percent win rates against them in 2025. So from a competitive standpoint, we're doing very, very well. We're very pleased with where we are. AI, of course, is a part of every conversation, right? People want to understand where are you going? How are you investing? How can we take advantage of AI with your platform. With our customers -- sorry, with our competitors, we've got some competitors that can tell a compelling AI story. But when our customers really break that story down and if the story starts with, well, first, we've got to move everything to a data lake, and then we've got to do all this data indexing, and we're going to perform functions over here and then move it back in. And we've got to think about security of the data lake and we've got to think about latency. None of that conversation happens with us. We turn it on and you use it. Again, we're versionless software. So every one of our customers is running the same version of the software, which means right now, every one of our customers has access to our AI agents. If they simply turn them on, they can use them today.

And I think there's been some debate about the cloud-based ERP migrations and if that's an opportunity for you guys. Any update on what you're seeing maybe from SAP or Oracle? Any kind of ERP replacement cycles?

Yes. So I think that ERP replacement cycle is always a tailwind for us, right? But I also think that -- because any time somebody is going through this process of, hey, I've got to relook at my whole SAP landscape because SAP is forcing me to do this upgrade, then it's an opportunity to think about best of breed and do they want to stop using SAP for WMS and come to us. And we've been very successful in that cycle. I will say, though, a lot of customers -- the dates continue to shift from SAP, for example. So that deadline doesn't feel as firm and it maybe isn't producing the tailwind that we thought it would as quickly as it is, but it's a constant tailwind...

Constant in the market.

Yes.

Sanjeev, maybe double-click a little bit on the AI products that you guys have announced. And dive into some of the functionality and what can that do for your customers?

Yes. So when we kind of think about AI themes, and I'll talk from a broad industry perspective on where AI can kind of impact software. The 4 areas we see. One is just user experience, right? When you think about how this is even the human computer interface, how it's evolved, right? It started from a punch card, went to a green screen, went to Internet, went to mobile, right? And now with the natural language interface, I think that overall, you will see the software just generally change in terms of how you converse with software and how humans interact with it. So that's kind of one piece we are focused on. And I think the overall industry should be focused on. And the second piece we talked about time to value, right? And how do we kind of accelerate our implementation, how do we reduce that time required to get somebody live and realize the value. So that's kind of another big area of focus for any package software, I guess, should be a pretty big part of focus. The third big piece, which kind of covers everything in some way is productivity, right? We're looking at every operator who uses our system and say, okay, how do we make them more productive? What actions can we automate? What can we kind of make better judgments so that those buses automate. And the fourth piece is around data insights, right? What can you get out of this data, which you have? What insights can you draw? And how can you kind of infer problems earlier and take actions. So those are kind of 4 broad themes of all the agents we are building. They probably fit in all of these 4 categories. We have agents in each of those categories. And I think these 4 broad topics I mentioned to you, I feel just as a general broad software industry, they are the right topics to think through and how do you make progress on those.

And so as we're thinking about supply chain, what are some early like 1 or 2 use cases that you would think would be the most interesting that would fit in one of those 4 buckets?

Sometimes I'll not kind of say there are some videos out there where there's kind of a little bit big sexy, big bullet things. Where I find is even solving small problems, can actually make a pretty big impact. I'll take an example, we publicly talked about Eaton, right? We built an agent. It took our forward deployment, uses about a week, 2 weeks to build that thing. And all it was really about was they had an exception process when something happens, somebody is constantly monitoring it, and then they will figure out how to chase the problem and fix the problem, right? The whole process we could automate in a week through an agent, and that increased their shipping by 3%, right? That's a pretty big impact to the...

In the first week...

In the first week, right? So it wasn't really a big large problem you had to solve. You can solve a lot of these exception problems, which is true across supply chain, right? Think about supply chain, there are a lot of small exceptions people deal with. And solving these small problems and add them up, they can make a pretty big impact, right? There will be big problems we'll solve, too, right?

But the way we solve that is when you subscribe to our AI pack, you get a standard set of agents that we built and we continue to build. We'll continue to add standard agents. But what's been really exciting is what you also get with our AI pack is the foundry, which allows you to either modify any of those agents and customize them for your operation or you can build an agent from scratch. And the one Sanjeev just mentioned was one that was built from scratch in about a week or 2 with our team.

And we see that pattern across our customer base, right? I mean I've talked to a lot of customers. They all have a spreadsheet on all of the exceptions they manage, how do they manage. They have a bunch of FTEs managing those exceptions. And then even more than saving the FTEs, right, it's really the impact of the underlying metric, right? The shipping on time, the fulfillment rates, all of those things start changing once you automate a lot of these things and speed them up.

And what about the opportunity to use that with other agents, right, across other systems? I know people maybe don't talk about Active Omni enough and what kind of data that could provide in terms of the whole e-commerce solution. But maybe talk about that more on the front-end customer side and less so maybe on the supply chain side.

Yes. I think just overall, AI will have a broader impact. And customer service side, obviously, has a pretty big impact. I think that's what people talk about the most talk about the most, right. [Technical Difficulty] So call center agent kind of trying to get when a customer calls in, just getting a quick summary of what this customer could be calling about, giving them the right answers to the call center agent right there in the face, summarizing the calls, figuring out what happened in the call, right? Those are all kind of really net additive to the customer satisfaction side of it, right? I've lost my train of thought just -- so from an Omni perspective, right, if you -- store manager, let's say, take a store as an example. So today, a lot of store managers are trying to figure how do they kind of uplift their shares. They don't have data. They don't know what's going on, with the foot traffic and everything else. We can analyze a lot of the data and start putting that out to the store manager upfront saying, this is what XYZ can do because store X is doing better, right? I'll go back to the call center topic for a second. One thing which we find really useful there is sometimes when you have these call centers, there's a very high turnover rate there. So you get a lot of junior guys, and they got a lot of experienced call center guys. Where we see AI kind of help really is bridge that gap between them, right? But a lot of these junior guys who do not know the SOP, who do not know how to kind of operate. Now with the help of AI and with the learnings that they get from the senior guys, we can kind of codify that. And then you can kind of feed to all these guys to take the real actions, right, so they can service the customer a lot better, right? So that's kind of another big impact where you can bridge the gap between the junior most salesperson to a very senior salesperson.

Got it. And you mentioned the retail store owner. I wanted to unpack point-of-sale a little bit. You've had some strong wins there. Where are we in the evolution of that business? And how do we think about that opportunity longer term?

Yes. So point-of-sale is one of the products we're really excited about going into '26. So if you remember, last year, we built a sales specialist team around point of sale as well as POS and others. But we're starting to see the fruits of that labor now in terms of the pipeline and the opportunity that we've got. We've never been disappointed with our win rate. Again, 70-plus win rate across all the products. It's just about getting more at bats and competing for more. And when we look at our pipeline across some of these products like POS and TMS and supply chain planning, it's the biggest invest that we've had going into a year. So really happy about where we are.

And maybe talk about how you're attacking that opportunity. I know there's been some go-to-market changes, partnership efforts, like maybe unpack what you've done in the last year.

Yes. So we built a sales specialist team. We've changed our partner model and a big focus on changing the partner model to really get more specific and prescriptive with our partners about what we expect from them and how they can help us grow and what we can do to help them grow in return. And part of that is in the POS space, if you think about all the POS deals that happen out there, our partners are Accenture and Deloitte and IBM, et cetera, et cetera, they are aware as a company. They are aware of every one of those deals that's out there. So we want the Manhattan team at those companies to be making sure that they are aware and bringing us into those deals. And so you think about all the things that we've done across all the different elements, the regionalization of some of these things has just expanded that opportunity for us.

And maybe just supply -- a similar question but on supply chain planning. I know that's a newer product. Where are we in the adoption of that?

Yes. So supply chain planning, we just rolled out in the cloud platform about a year ago. So it's our newest product on the cloud platform. And I think we've said every quarter that it's moving faster than we had kind of anticipated. And I think the most exciting thing about supply chain planning is we've now seen multiple customers become new logo Manhattan customers with supply chain planning. And in the beginning, we kind of expected, hey, that's going to be a logical add-on if you're a warehouse or transportation or order management, add the supply chain planning. But when we see new customers come in and start with supply chain planning, that tells us 2 things. Number one, we've built a product that can compete head-to-head against all these other your pure-play supply chain planning products. But number two, it tells us they're coming to us because they want to bring more to us later. They see the value of this platform.

So we talked about this last year actually. So the confidence that you've talked about to, say, 20% plus growth in cloud, that's well above industry averages that we see. So talk about the visibility that you have. I know we talked about the products, but the confidence that gives you to say, hey, we can grow our cloud revenue 20%.

Yes. No, I think, again, you look at the things that we've done, and we started these things in '25 to make sure that we were set up for this in '26, but put Bob Howell as our Chief Sales Officer, so he could standardize some things globally, build out that sales specialist capability across each one of our products globally. We brought in Greg Betz from Microsoft to really get us focused on a conversion program that was -- historically, we've said people will convert to the cloud when they're ready. And now we're just making sure that we're having that conversation with every one of our on-prem customers so they understand the difference between what they have and they could be and also so that they understand how easy it is to make that conversion. Greg and his team are also focused on this renewal program where we make sure that we're maximizing not only the price uplift, but the cross-sell opportunity at the time of renewal. And then recently, we added a new Chief Marketing Officer, who's come in and really helping the team think differently about how we create pipeline and create opportunities, partner with our partners to really get a different level of market awareness across all of our products.

And specifically on the conversion side, like how important do you think this fixed services dynamic can be for customers? Is that -- could that be a real catalyst in terms of moving people over to the cloud?

Absolutely. Because if you think about all of these customers that are on-prem, they have this pattern in their head, right? Every 5 to 10 years, they do a massive upgrade. And that upgrade is a big project. And it's like open-heart surgery, right? The last one they did probably ran long, probably was over budget. They probably still have -- feel pain from that process. So going to them and helping them understand that, hey, this one is much easier. We know what you're running. We know where you're going. We've built automation. We've built AI. It can happen fast. It's a low cost. And by the way, it's the last time you'll ever do it because after you do it, you're on versionless software. When we have that kind of conversation, some of these companies that were trying to make this -- forget about this pain and pushed off as long as possible. Now they're saying, okay, let's talk about this. That makes sense.

Well, and I want to expand on that a little bit and Sanjeev, feel free to weigh in. So you talked about versionless and not needing to upgrade again. But 80% of the customer, well, 78%, keeping honest on the math, are still on-premise, right? So they need to go through a migration. But what is the value that you can provide them, not if it's just WMS, but what about OMS and TMS? And you're selling them multiple supply chain solutions. So what do you think that you can sell to them over time as they're -- again, early in the journey, but the value of the integrated platform?

Yes. I'll jump in first, and then I'll let you go because I think that's a really important question. When we get our on-prem warehouse customers to convert to the cloud, it immediately opens the opportunity to cross-sell transportation, cross-sell supply chain planning, upsell the AI packs. All of these cross-sell upsell opportunities don't exist in the on-prem. Now I want to also say that 55% of what we sold last year was new logo. So we've always been a believer that, hey, if we can expand our share, every one of those new logo customers, we're creating those same cross-sell and upsell opportunities. But now we've got a sales force that's built to go do both, which we've never had before.

Yes. I mean I'll add. A lot of times, customers who are still on on-prem, there has been an inertia on moving, and you need some sort of a force multiplier to help them kind of find a reason to move. I think AI would be a pretty big incentive for them to say, okay, I want to move from X to Y, and this is what I will get out of it when I move, right? So that's kind of a big large factor on how we can overcome that inertia of movement. And once you kind of move to Eric's point, right, it becomes a lot more easier to kind of use the rest of our products, which are already there on the platform.

Maybe you said AI from a different perspective, but would love to understand how you guys are using it internally and what efficiencies you've been able to get from AI.

Yes. So we've been using AI internally for the last 3 years almost now, right, almost every function of ours. I'll take our ops, DevOps side of it, right? We've grown our customer base in the last 2, 3 years. Our number of AIs, which have gone live is probably about 2, 2.5x. DevOps team is completely flat. We have not grown our headcount there at all, right? And a lot of that has come from using AI to kind of manage those processes to make sure that we can continue to keep that right. We use it for our development. I mean I talked about code generation. Of course, we kind of wrote our own generator. But the remaining portion of it now, we're starting to use a lot more AI in that. Our customer service organization, same thing trying to -- when they get a trouble ticket, they're trying to figure out, okay, is this happened before and AI is kind of helping with that aspects. Services, we talk about time to value. I mean our services team is using it extensively to figure out how do they reduce that total time it takes to implement the software. That's a pretty big piece. So I would say, I mean, it's pretty broadly used across the company, and we're using more and more.

And our sales team is using it. They're using it to get intel on opportunities, potential customers that we're going after. They're also using it to do better, more applicable demos faster in the cycle. So they can actually show the customers exactly how you're going to use our software very early in the sales cycle.

And we can respond to more RFPs, for example, in the sales team, right? I mean they're filling 80% of the RFP using this. So human kind of reviews it, but it takes a lot of that mundane task away.

And so I want to understand the sales side in terms of staffing and hiring. Do you feel like you're where you need to be to go attack the opportunity in front of you?

So our sales team is massively more efficient today than they were a year ago, and we're still going to add more because the opportunity is there. The opportunity across all of those products, conversion, new logo, cross-sell, the opportunity is there. So we're continuing to invest in sales and marketing.

And maybe just last one to wrap up here, but you guys generate a significant amount of cash. Obviously, there's investment in R&D, but I would love to understand how you're thinking about priorities for deploying that cash?

So no change there. We've always said if there were an M&A opportunity that made sense, we wouldn't hesitate to look at it. But again, I think a big part of the strategic advantage position that we're in right now is because Sanjeev and the team have really stuck to this platform approach. We haven't bought anything and bolted it on and integrated it. So if we were to acquire something, we'd have to rewrite it. And we feel like we can write it ourselves faster than that. So M&A isn't a big part of our strategy, which means buybacks, big focus on buybacks.

All right. We'll wrap it up there. Everyone, thank you.

Thank you.