Kinaxis Inc. (KXS) Earnings Call Transcript & Summary

Very well, thank you all for joining. My name is Thanos Moschopoulos. I'm a research analyst at BMO, and we're happy to have Kinaxis here with us today. Joining us are Razat, the CEO; and Herb, who just joined the company as CFO. Kinaxis reported strong Q2 results yesterday with accelerating SaaS revenue and ARR growth, and we'll hear from management about some of the factors that are driving that. And if anybody has any questions, feel free to submit them through the webcast portal. I'll be keeping an eye out on those throughout the discussion.

So, Razat, to kick things off for investors who are newer to Kinaxis, can you explain very briefly what your software actually does and who some of the clients are that use it?

Yes, sure, Thanos. Thank you so much. Look, we at Kinaxis, we're a supply chain planning, decisioning and orchestration platform -- software platform. And we are continuously sensing all the changes that are happening in the supply chain. We've got very smart predictive, prescriptive, agentic-driven capabilities to help companies make decisions in navigating through all those changes. And now as we're expanding our footprint, we're also getting into how do they then action those decisions and orchestrate those decisions in a way that achieves their outcomes. So that's what we do. We play in 7 verticals with companies that have physical supply chains. So we are working with the likes of Ford Motor Company, General Motors, Qualcomm, Chanel, Merck, Unilever. So lots of large companies with complex supply chains. That's sort of our sweet spot. And we're a company who really is -- we're very focused in getting data from the outside-in signals and across the enterprise. But really our core competency and the value we add for our customers is really oriented around making these complex trade-off decisions through scenario planning, through deep optimization, machine learning algorithms, heuristics and, of course, more and more through agentic workflows. That's really sort of our bread and butter. We're not kind of a regular SaaS company that is scaling based on the number of users or seats. It's really a planning and decisioning engine and more and more going forward an orchestration engine. That's what we do. We're headquartered in Ottawa with a very large global footprint around the world. Do you want to add to that, Herb?

Yes. I think -- thank you, Razat. The only thing I would add, Thanos, to what Razat said is I think one of the things that really drew me to the company is that what our customers use the product for is to make decisions that have very real direct measurable impact to their financial statements. So always trying to understand how much demand do I have? How am I going to supply it at what cost that has a direct impact on revenue versus margin, working capital. So these are very complex problems and importantly, problems that have very measurable ROI if you solve them the right way. And that's what excited me about what the company does and why I see tremendous value that it provides to the customers.

And to clarify, for the customers you mentioned, you're like system of record for planning, right? Not some -- not some add-on, you're like the system for supply chain planning within those...

We are -- so when any of our customers, they need to understand what is their forecast for a given product or what is their production plan for a given factory or what is their supply plan for the network. All of those are derived and calculated using our algorithmic engines, and we are the system of record for that. We're also the system of record for all the costs and constraints and all the operational details of how the supply chain functions, right? So -- and we model that in our digital model. And to Herb's point, we are planning over $300 billion of inventory across our customer base, over 0.5 billion parts get planned. So a lot of our customers are highly dependent on our mission-critical platform for making those high-stake, big-stake decisions that lead to hundreds of millions of dollars, sometimes billions of dollars of impact for our customers.

Great. So growth has clearly been accelerating in recent quarters, and you raised your SaaS guidance yesterday. It seems like part of that is a healthy demand backdrop for what you do, just given all the macro volatility. Part of that, I think, is Kinaxis specific. Maybe let's just touch on the demand backdrop first. So with all the supply chain constraints, macro volatility and so forth, does that mean quicker sales cycles? Does that mean clients have more budgets? To what extent are they open to doing something big and transformative versus looking for like a cheap band-aid solution to address their problems?

Yes. We've seen only a small marginal reduction in our length of our sales cycle. So I'm not seeing that change a whole lot. But we're definitely seeing a sense of urgency from our customers. So Q2 was a good example of that. We saw the same thing in Q1 as well and frankly, in Q4 of last year, right, is that customers are prioritizing their investments in our platform because of all the changes that are happening in their supply and demand. And again, it's also important to understand what is causing all this volatility and uncertainty. Of course, a lot of that is regulatory and tariff and geopolitics and wars, but those are not the only factors, right? There are other factors that are driving the changes in demand and supply volatility. Like, for example, the whole build-out of the data center value chain, right? That's impacting energy utility companies, cooling unit manufacturing companies, semiconductor companies and so on and so forth, right? We work with all of them now, right? Or for example, what's happening in the consumer manufacturing space with shifting consumer expectations and consumer buying patterns that are requiring new fulfillment models or what's happening, for example, in the pharmaceutical industry with new drugs and new introduction of new medicines. And all of these different things, as much as we'd like less change in the world, the reality is there's always more and more change happening. And as a result of that, there's more demand for the need for a platform like ours that is able to sense all those changes and then make smart decisions that are very consequential like we talked about. I think the second reason for the demand, I think, profile is just the substance of our offering and our product offerings has expanded significantly, right? We've had an aggressive product road map. We've increased our R&D spend coming into this year significantly as well. And that's allowing us to not just land net new logos where we have much larger deal sizes that we are closing, but also it's leading to us being able to cross-sell to the existing customer base as well. So things like all our agentic capabilities are great cross-sell opportunities for us right now. Our machine learning-based forecasting capabilities that we've introduced, advanced inventory optimization capabilities, enterprise scheduling that is using genetic algorithms, right? All of these different things are important ways for us to expand in existing customer base, but also to close larger opportunities in net new logos. And then the third reason is we've just transformed our go-to-market engine in a very significant way in the last 18 months, as you know, Thanos, right? And that has led to us transforming our ways of generating demand, managing pipeline, executing on pursuit cycles. And we're just winning more business. Our win rates, our competitive win rates are at an all-time high. Our pipeline yield rates have been subsequently -- sequentially improving every quarter. And so I think it's a combination of these 3 things, the macro environment with all the changes happening in the world, the substance of our product offering expanding and growing and our execution in the field as well.

On the win rate to drill into that, I mean, the industry feedback I heard, including some of your partners is that your competitive position is essentially the best it's ever been. So why would you say that is -- I mean, why has this company had such a durable competitive moat over the last few years when you're competing against some larger well-funded businesses and yet your lead seems to be increasing. So why has that been the case?

Yes. A few things, right? One, the business we're in, we're solving really complex problems, right? I mean we're not solving simple problems. So you need a lot of depth of domain knowledge and understanding of the physics of the supply chain. And that domain knowledge is not just in our people or the talent we have, it's also embedded in our platform that we have, right, which is the richest representation of the physics of the supply chain and all the constraints and operational considerations and policies and interdependencies. That's a big moat for us is in being able to reflect the physics of the supply chain in our platform. And I think the third reason is, I think, just our focus on customer success and value delivery, right? We just had a much better track record than our competitors, several of our competitors in not just selling, but really innovating with new products, selling, but also delivering value to the customers. And that's built trust over time. And that -- by the way, that trust is super important as we're getting into the agentic era.

I think my observation would be that just with the increased volatility, it's become more apparent what platforms work, what doesn't? And then maybe that's played to your strength. Would you agree with that?

That definitely works to our strength because if supply chains were just very static, nothing was changing in the demand and supply profile, it would be questionable why you would need Kinaxis. But frankly, that's not the environment that any of our customers are living in right now.

And if we just talk about, I guess, the overall market, sometimes it's surprising to see some of these logos that you're signing, they're coming to you, they didn't already have a sophisticated solution in place. When you look at the market and what proportion are still using Microsoft Excel versus having maybe a configure platform or legacy platform, how much runway and opportunity is left versus people already have a solution?

Yes, that's a good question, Thanos. Look, I mean, even when we are replacing old legacy systems for planning from other players, even there, what we find is, over time, the organization has been using spreadsheets to do their planning, right? So they may have a planning engine in theory, but in practice, where the planning is happening and all the scenario analysis is happening is in spreadsheets, right? And so it's a journey. We've got several organizations that are surprisingly large and complex that still have been running on spreadsheets, right, where we are putting in place for the first time an integrated demand planning, supply planning, inventory planning, S&OP sort of capabilities. Those are foundational. But there are also many examples of companies that have done that, but are looking for the next wave of productivity improvements as well, right? And saying, look, we've done the basic demand supply planning. One, our systems are too rigid. And two, we want to get into the agentic era, and we want to transform our ways of working. We want to transform our ways of decision-making and governance. So I think in those situations also, we do very well competitively, right? Because we've got a modern platform that's a single architecture, single code base, single data model. So we're not a mishmash of acquired assets with different tech stacks like a lot of our competitors are. But also in addition to that, we have really ingested Maestro -- agentic capabilities within Maestro, which is our platform. And that's becoming a bigger and bigger factor as we're competing for net new logos because that's becoming a bigger part of the evaluation process, not just to put the basic foundational planning elements in place where we have leadership position and Gartner and others have validated that, but also we can bring them on the journey to transform into the agentic era. So they don't want to just replace a legacy system with a legacy process. They also want to future-proof their innovation because it has a big impact on their working capital efficiencies, their operating costs, their cost to serve, their service levels with their customers. So all of those reasons are important as we are winning new business.

Let's segue to AI since you raised that. So on the call yesterday, you said 10% of your clients are now using Maestro agents whether paid or trials. What are some of the initial use cases where clients are getting the most value? Give us some examples maybe in that regard.

Yes, sure. So just to back up a little bit. So as you know, earlier this year, we launched our agentic capabilities within Maestro. We had a more controlled sort of plane with 7 customers. We talked about that in Q1 of this year, right, where we were working with early adopters and we made those customers successful. And we went through a lot of learning cycles on the customer side and on our side as well. And then more recently, in the last few months, we've really opened the aperture to make it available to the broader customer base as well. And we're getting a lot of good traction, and that's what's led to roughly 10% of our customers are in trial or in paid mode. By the way, even a lot of those trial periods are all paid. So most of those are paid customers, just to be very clear. But in terms of the use cases, look, there's a whole plethora of use cases that are emerging, but I'll give you a couple of examples, right? So one of the largest fashion companies, health and beauty companies in Europe, they've got a very volatile demand profile, right? So their consumer base is constantly making changes. They're very promotions driven, but they're also very social sentiment driven. So if a celebrity uses their lipstick product, for example, and makes a post on social media, that can lead to a massive surge in demand, for example, right? And then they have to plan all their inventory and their supplies to service that demand as an example. So we've worked with them, and they were already using Maestro for demand planning and supply planning. And now the agentic capabilities we've enabled is really scanning the different social media feeds and is transforming what elements from the news items that are coming in are relevant that will impact or surge demand. And by the way, it's doing it not an hour a day or 8 hours a day, it's doing it in an always-on mode because the agent works 24/7, right? And then with that sort of sensing logic, it is also then defining what is the plan versus actual deviance because they have a rolling 12-, 18-month demand profile and a demand forecast. But with these surges in demand, it's quickly assessing what is the impact of that demand. And then it is surfacing the impact of the plan versus actuals to say, here are some scenarios of how we can best service that demand because we have that scenario planning framework. So all the supply plans and the inventory is also residing in Maestro. And it's by the way doing that in an automated way. And now that is getting surfaced to the human user who can say, you know what, I know that this is going to surge demand because somebody has posted this on social media. I know what the impact is. And now here are the 3 options I have to take remedial action as a result of that. Doing all of that would be -- would take 10x the amount of time in the past and 10x the number of resources in the past, right? And now all of that can be automated through the agentic capabilities. So that's an example on the demand side, right? There are other examples on the supply side because a lot of our customers, they're highly dependent on getting component parts or raw materials from their suppliers to do their manufacturing, their assembly, their testing, right? And so we've done some analysis where some of our customers want to have an understanding of what is the inventory on their supplier side. And what is -- what are the shippers that are coming in. And there, they're constantly scanning what is the risk profile. And in the past, they would have teams or armies of analysts doing that. Now you -- we can identify that. Again, that agent on the supply inventory risk side is modeling the risk and then surfacing that to the human personas. And also, again, saying, based on this risk, this is the kind of production or the supply plans that are going to be impacted. And oh, by the way, it can take it all the way down to the orders that are pegged against that supply plan, right? So this customer's potential delivery date could be impacted as a result of that. Again, those are needle in the haystack kind of problems given the plethora of systems. But because we have that entire network modeled in Maestro, now these agents are able to do that in a very seamless way on an ongoing -- again, in an always on mode. And there's a whole bunch of other scenarios. Now what we are doing is we are developing agent skills that are relevant to the planning and decisioning platform we have. But we've also embedded within Maestro an agent studio. So customers and partners and our own teams can compose the agents as well, right, on their own, and they have access to all the data and resources that resides within Maestro. We had a hackathon just last month on this with our customers and partners. It got massively oversubscribed. We've had to schedule 3 more hackathon sessions just to accommodate the interest level. So while it's in early days, I'm super excited about -- and in many cases, our customers and even our partners are surprising us in how they're using our solutions to model and compose these agents to drive productivity, to drive better decisioning, to drive smarter risk analysis, to drive better approaches to sensing and responding to these changes that are always going on in their supply chains.

And so the reason that I would be building agents in your agent studio or using your Maestro agents versus some third-party agents is because fundamentally, you are the system of record, you have the planning engine, you understand all the interdependencies in supply chain, correct?

You're right. But just to be clear, right? So yes, we have a system of record. But more important than that, we have modeled the physics of the supply chain. So it's like, let's say you drop a Tesla car in a jungle. As with all the automated automation, that Tesla cars aren't going to go very far, right? You need pave roads, you need guardrails, you need traffic routes. And then that autonomous vehicle performs really well, right? It's the same thing in the world of agents. If you just land agents in the jungle and plethora of data that exists across these organizations, it's not going to go very far. And so we are providing in our platform, the ability for organizations and these agents to do smart things because we have reflected the physics of the supply chain, right? And at the same time, I fully expect that customers will use the agentic capabilities we have using our platform, but also customers will use agents from other systems or their own DIY mode. But again, using our platform and the brains and the planning and decisioning engines we have in our platform. But that's the good news. And even when they do that, we have a way to monetize it given our Maestro activity unit-based pricing structure.

Maybe my last question on AI before we move on is just how do we think about the potential revenue opportunity from these Maestro agents? I mean, maybe early days, but could we be thinking about like a 10% to 20% uplift to your existing contract size? Or is it just too early to say?

Yes. Look, I think I would segment that into 3 parts, right? The first part of our agentic journey has been just in taking the LLMs, creating RAGs, retrieval-augmented generative capabilities using data and documents, building agent skills. So now customers have a conversational interface to our application. They don't have to point and click. They can do a lot of that through a natural language interface. To me, that's table stakes. And I don't expect that to generate a whole lot of incremental revenue because our customers are almost like expecting that going forward, right? So that's the first part. And we've already done that now. The second part, which is in really packaging agents and having this agent thick studio, that's leading to all kinds of interesting productivity improvements, all kinds of interesting use cases like the ones I just mentioned earlier. Those have incremental value, right? And there, we definitely see a lot of cross-selling opportunities. And more and more in most of the large deals we are closing, we're having a bundle of activity units dedicated to agents as part of the sizing and the pricing and the licensing to our customers. And that's pretty exciting because that's incremental revenue. But then there's going to be a third piece of this, which to me would be, frankly, the -- in my humble opinion, would be the largest revenue driver for us, which is what we're doing with our agentic orchestration platform, right, what we are building out together with our FDEs, where we're saying we are not just going to stop at the planning and decision-making, but we're going to help bridge the gap between planning and execution using this agentic framework. But in order to do that, we've got to be able to ingest data from other systems, other execution systems like sourcing systems, transportation management systems, warehouse management systems and ingesting data through those systems, mapping it into a common semantic layer and then the agents then traverse the graph that has that semantic context, right? And so that, to me, is going to be leading to the biggest value generation for our customers because it's going to transform the way that they achieve their outcomes for getting working capital efficiencies, getting operating cost reductions, improving service levels in a very profound way and it's going to allow us to engage with them to stitch together these orchestration use cases using the different LEGO blocks we have and this agentic infrastructure that we have, right, while leveraging all the brains we have of the supply chain and while leveraging the physics of the supply chain that we've modeled. That third phase, we are just starting, right? We launched it, as you know, at Connections in early June. We've been mobilizing our FDE capacity and infrastructure. We're also working with partners to scale up the FDEs even further. We are building out this broader platform. And by the end of this year, going into next year, we want to make sure we have some early reference customers. But those are going to be far more high value-generating larger deal opportunities than just the incremental cross-sell that we're doing right now.

So is the right way to think about it is you've got your existing planning market where you're the industry leader, you're taking share, lots of runway in demand. But now this is kind of this whole new addressable market opportunity that orchestration is going to open for you. Is that the right way to think about it?

It's a significant TAM expansion for us, Thanos, right? And we discussed this with our Customer Advisory Board, by the way, a couple of months ago, and we sort of put the rhetorical question in front of them and say, look, do we earn the right to expand into orchestration? And the resounding answer was absolutely yes because you cannot orchestrate without having the brains, which is in the planning engine, right? And so the lines given the new data architectures, given agentic AI, the lines are blurring between planning and execution and planning and orchestration. We are still going to have multiple systems of record underpinning it, right? In supply chain, there is no one single system of record. There are multiple systems of record. But how you evolve into the systems of intelligence and system of action is what we are doing. We are one of those systems of record today for planning, right? But the opportunity we have as we expand into orchestration is to become a system of action as well. And that's a really significant TAM expansion opportunity for us.

Let's talk about the go-to-market because I think it's also been a key part of the story over the past year or 2 in terms of some of the investments and initiatives there. I mean, so you're looking at expanding the sales force. You talked about investing further in partner enablement. You're launching the FDE strategy. Maybe just give us an update in terms of what you're doing on each of those fronts?

Yes, absolutely. Look, the first thing I'll say is, again, we are in the business of solving really complex problems that generate a ton of value for our customers. So as we are expanding our field team and our go-to-market team, we're being very thoughtful about the skills and the caliber of our teams to make sure that we are able to continue engaging with our customers in a way that creates value for them and creates those long-term sustainable sort of relationships with them, right? Our sales team has transformed significantly, I would say, in the last 18 months. And we are in the process, and that's a big reason why we are executing so well in terms of our win rates and pipeline new rates. But now we're also adding quota-carrying capacity. We're being very thoughtful about where we're doing that. We still play in 7 verticals. We think there's plenty of room for growth in those 7 verticals. We are seeing a little bit of expansion there given some of the challenges in value chains like the data center value chains. Like -- so if you asked me a few years ago, would Kinaxis play and be working with energy and utility companies, that wasn't in our top 5 list. Now those energy and utility companies have significant supply chain challenges as they're trying to service the data center build-outs as an example, right? And we'll always be evaluating incremental verticals as we go forward in the coming 3 to 5 years. But we're adding quota-carrying capacity in a thoughtful way. Our North America go-to-market engine is really humming really well. We've got a lot of growth happening in Western Europe as well. We are pretty concentrated. We're doing a lot of things in Asia Pacific, particularly in Japan, Taiwan and India. India is really showing a lot of interest and activity and pipeline right now. We are working with some of the leaders in that market there as well. And I think in my assessment, in my early assessment, I think they'll be the faster early adopters of our agentic AI and agentic orchestration capabilities. So look, I think we're humming on all cylinders, but a big part of what we are focusing on in the second half of this year is to thoughtfully add the right skills to expand our go-to-market reach and coverage model as we go into 2027, so we can continue to sustain the growth momentum.

And let's touch on your strategy around forward deployed engineers, FDEs. So when -- I mean, you're just starting to staff up there. When do we start seeing some of the benefits you think in terms of revenue acceleration? And just to clarify the revenue model, you're not charging billable hours, my understanding. Maybe just clarify how you monetize that?

Yes, definitely. And I'll explain that and would love for Herb to add as well on the sort of the unit economics around that, right? So firstly, why are we launching this FDE engagement model, right, and engagement motion with our customers. It's really to unlock the value potential with agentic AI, right? So our traditional planning engagement with our customers is fundamentally different from the agentic motion with the FDEs. And let me explain the difference, right? In a traditional model, the customers come to us with a set of requirements. Here are the features and functions you need. We scope it out. We have a project plan with our partners, with ourselves. And we deliver. There's a start and there's a finish the project, there's a go-live and then we support the customer to make sure they're getting the value, right? So that's been our model for many years. Now I'm simplifying here, but even in that model, the problems we are solving for our customers are pretty complex and very algorithmically driven in nature, right? However, customers that have done that, the phase that they are in, what we are finding is they still have some pain points, and they want to look for the next wave of productivity improvement. They want to look at the next wave of working capital efficiencies. And so they have outcomes that they want, but they don't necessarily know what exactly will be the capabilities and features and functions that will get them there, right? So we have to bring that engineering mindset, that product mindset to our customers in our customers' engagement model. And we -- in working in a very agile iterative way with our customers, we are defining what feature sets are required to help them develop the next wave of value, right? And that's where the FDEs come in. And this engagement model has a start, of course, but there's no finish because it's an ongoing engagement model. It's like a product-oriented engagement model as opposed to a project-driven engagement model, right? And it's very driven by pain points and outcomes. And it requires a little bit of a different skill set in how you are able to -- it's like a simpler skill set to our product managers, for example, that develop our products. So in terms of the packaging for this, the way we are doing it is this is not a services engagement. This is not a PS engagement. This is -- or the focus is around billable hours. This is really about packaging our base platform, the usage-based structure we have, which is the same activity unit-based structure we have and FDE resourcing bundled into one ongoing subscription. And again, we expect this to be multiyear subscription deals. That's the way we are packaging it. And frankly, while we're in early days since we launched this about 1.5 months ago, we're seeing a lot of interest and traction, and we're now getting into proposal phase with the early set of customers. And we're making sure we have the right skill sets and the capacity, partly ourselves, but also partly with scaling with our partners that have similar skill sets as well. Do you want to add to that, Herb?

Yes. I think the only thing I would add, Thanos, to what Razat said, and it's a very, very important distinction because I think a lot of them -- there's a lot of confusion where people think, oh, this is like a traditional time and materials, billable hours type of model. And that's absolutely not what it is. This is really about working closely with the customer to make sure the right solution, meaning our platform, everything that we're building in the decisioning and orchestration is delivered as quickly as possible to the customer so that they realize value. And for that reason, our expectation is that the way this will be viewed from a customer perspective is value-based, value-driven. We would expect to get those types of economics from the customers. And the way that will then get reflected in our own financial statements would be something that looks just like a traditional SaaS or subscription revenue ratable recognition.

And competitively, how differentiated is this? Is RFPs becoming table stakes? Or does this help your competitive position? Or is it more about just making the client better aware of the competitive advantages technically that you have in the platform?