Descartes Systems Group Inc. (The)

Good afternoon, ladies and gentlemen, and welcome to the Descartes System Group Quarterly Results Conference Call. At this time, all lines are in listen-only mode. Following the presentation, we will conduct a question-and-answer session. If at any time during this call you require immediate assistance, please press star zero for the operator. This call is being recorded on Wednesday, March 11 of 2026. I would now like to turn the conference over to Mr. Scott Pagan. Please go ahead, sir.

Thanks, and good afternoon, everyone. Joining me in person on the call today are Ed Ryan, CEO, Alan Brett, CFO, and Ed Gardner, EVP, Corporate Development. And I trust that everyone has received a copy of our financial results press release that was issued earlier today. Portions of today's call, other than historical performance, include statements of forward-looking information within the meaning of applicable securities laws. These statements are made under the safe harbor provisions of those laws. These forward-looking statements include statements related to our assessment of the current and future impact of geopolitical, trade, tariff, and economic uncertainty on our business and financial condition, Descartes operating performance, financial results, and condition, cash flow and use of cash, business outlook, baseline revenues, baseline operating expenses, and baseline calibration, anticipated and potential revenue losses and gains, anticipated recognition of revenues and occurrence of expenses, potential acquisitions and acquisition strategy, cost reduction and integration initiatives, timing of management changes, the approval and potential share purchase under a normal course issuer bid, and other matters that may constitute forward-looking statements. These forward-looking statements involve known and unknown risks, uncertainties, assumptions, and other factors that may cause the actual results, performance, or achievements of Descartes to differ materially from the anticipated results, performance, or achievements implied by such forward-looking statements. These factors are outlined in the press release and in the section entitled Certain Factors That May Affect Future Results in Documents Filed and Furnished with the Securities and Exchange Commission, the Ontario Securities Commission, and other securities commissions across Canada. including our management's discussion and analysis and annual information form filed today. We provide forward-looking statements solely for the purpose of providing information about management's current expectations and plans relating to the future. Your caution that such information may not be appropriate for other purposes. We don't undertake or accept any obligation or undertaking to release publicly any updates or revisions to any forward-looking statements to reflect any change in our expectations or any change in events, conditions, assumptions, or circumstances on which any such statement is based, except as required by law. And with that, let me turn the call over to Ed.

Thanks, Scott, and welcome everyone to the call. Today, we're reporting record quarterly and annual financial results across the board. We're ahead of our annual plans and finished the year extremely strong. These are great results that I'm looking forward to walking through in more detail. However, first, let me give you a roadmap for this call. First, I'll start by hitting some highlights of last quarter, provide some comments on how artificial intelligence impacts our sector and business. I'll then hand it over to Alan and Ed Gardner, who will go over the Q4 annual financial results in more detail. After that, I'll come back and provide an update on how we see the current business environment and how our business was calibrated for Q1. and then we'll open it up to the operator to coordinate the Q&A portion of the call. So let's start with the fourth quarter and year that ended January 31st. Key metrics we monitor include revenues, profits, cash flow from operations, operating margins, and returns on our investment. For this past quarter, we again had record performance in each of those areas. Total revenues were at a record high of $192.8 million, up 15% from a year ago. Record high services revenues were also up 15% from a year ago with our continued focus on generating recurring revenues. Record net income was up 22% from a year ago. Record income from operations was up 25% from a year ago. Record adjusted EBITDA was up 18% from a year ago. Our adjusted EBITDA margin is at a record high level of 46%. We generated a record high of $76 million in cash from our operations. up 25% from a year ago. So strong record results across all these key metrics. For the year, the results are equally impressive. Record revenues up 12% with service revenues up 15%. Record net income up 14%. Record income from operations up 16%. Record adjusted EBITDA up 16%. Record cash from operations up 21%. As I said, all results for the year were ahead of our plans. At the end of the year, we had $356 million in cash and we were debt free with an undrawn $350 million line of credit. We remain well capitalized, cash generating, growing, and ready to continue to invest in our business. We also have a normal course issuer bid that allows us to purchase up to 8.6 million shares before December of 2026. We made some small initial purchases before we went into a trading blackout in January. So a tool that we have that we've already used and that may be used again as we monitor rather volatile recent market conditions. I'm not going to spend much time talking about the operating results for the quarter or year. As I said, they were very strong. The reasons why are similar to what we've done in the past in the previous two quarters. First, we saw strength in the global trade data and intelligence from a chaotic tariff and sanctions environment. Second, we saw strength in real-time visibility and shipment tracking as we continue to leverage AI tools and agents to have the industry's leading shipment tracking rates. And third, we saw strong e-commerce imports into the United States, which was good for us as we have the market's leading solutions for helping with high-volume rapid customer appliances. We also completed a tuck-in acquisition in our e-commerce pillar earlier today, UK-based OrderMind is a current partner of Descartes, with its solutions already working alongside our PeopleVox e-commerce WMS for UK customers. We're particularly excited to introduce OrderMind's core product, ForecastMind, to our e-commerce customers around the world. ForecastMind is a strategic step forward in our e-commerce AI investments, accelerating our AI-powered forecasting and demand planning, particularly for e-commerce sellers using Shopify. E-commerce sellers are focused on freeing up cash, protecting margin, and scaling without operational drag. We've got a huge pool of rich inventory, order, supplier, fulfillment, and returns data that ForecastMind can train on to help sellers do this. Specifically, it helps convert e-commerce signals into clear, actionable insights that reduce excess inventory, prevent stockouts, improve forecast accuracy across channels, and seasonality efficiency. automate purchasing, and shorten planning cycles from days to minutes. I'd like to welcome the OrderMind team to Descartes. I'm happy to answer questions on any aspect of our operations or the acquisition later in the call or afterwards. However, I wanted to spend some time today with some comments about what I'm getting asked the most questions about the impact of artificial intelligence on our industry and on our business. I've heard market commentators raise concerns about the impact of artificial intelligence technologies on the long-term prospects and terminal values of technology businesses, specifically that if AI can generate software code, then coding becomes commoditized and established technology companies are vulnerable to new entrant competitors or companies taking software coding in-house. In short, the headlines are AI could kill existing technology companies. I don't buy any of that. Those commentators are fundamentally misunderstanding the value technology companies bring to customers. I believe AI technologies will make businesses like Descartes even more valuable to customers. At their heart, technology businesses do not exist to just make software code. This is not the value they deliver to customers. Instead, they deliver a comprehensive service to customers, a service that includes security, trust, stability, compliance, infrastructure, operational and customer support, workflow and domain expertise, proprietary data, connections, scale, innovation, cross-pollination of valuable ideas, and yes, technology functionality that is powered by software. Generative AI doesn't replace all those things. Generative AI is a tool that allows tech companies to make many of those things better and deliver them faster. Tech companies will leverage AI to make their businesses more secure with AI tools that help identify, diagnose, and prevent attacks. They'll make their service availability more reliable with AI tools across their infrastructure, and they'll enhance operational support for customers with AI-powered agents to address routine inquiries. Generative AI companies know the market is making a mistake by questioning the long-term prospects of technology companies. They're admitting that publicly. and they're running their own businesses by relying on specialist technology businesses rather than using their own IT tools to build an inferior quality enterprise system. The market spheres don't match reality. I've also seen market commentators paint many technology businesses with the same brush, that the impact of AI will be the same for everyone. That's also not true. The impact of AI will vary significantly by industry, business model, scale, pricing model, and investment commitments. So let me talk to Descartes specifically. What we are, what protects us, the mode around our business, and the opportunities that AI brings to our business. First, what we are. Descartes is a network services business. We run the global logistics network. It's the world's largest connected community of supply chain and logistics participants formed over more than 30 years. It's relied on by the community every day to process billions of transactions per year. We use technology to help our customers solve complicated enterprise supply chain and logistics problems on the GLN that require the cooperation of multiple parties and communities on the network. We're not an enterprise software company. Now let me describe the huge moat we have protecting our business. I'm going to try and help you wrap your arms around how broad it is by putting it into some key categories. First, we're a critical network relied on by the world. As I said, the Global Logistics Network is relied on by the community every day to process billions of transactions per year. Its unparalleled uptime, breadth, speed, and specialization make it critical to the operations of our more than 30,000 customers. It's difficult and time-consuming for another company to replicate that. It's challenging for our customers to find something as reliable, relevant, and comprehensive to switch to. It's the major moat for our business. Two, we help solve complicated inter-enterprise challenges. Supply chain and logistics challenges are not enterprise issues. They can't be solved with just a customer's internal people and data. Supply chain and logistics challenges require interaction with external parties beyond the enterprise, whether they be drivers, warehouses, customs authorities, governments, shippers, carriers, logistics intermediaries, or banks. If you use AI to create tools or applications to help you, you still need to connect to the supply chain and the logistics world. That means you either connect once to the GLN or you connect and maintain hundreds or thousands of connections to external parties by yourself. The enterprise nature of our business keeps us relevant and makes us a better choice for our customers than trying to do it themselves. Third, we process transactions. Descartes is primarily a transaction processor. As things are processed by the GLM, such as tenders, bookings, loads, invoices, bills of waiting, customs filings, security filings, et cetera, we charge for our services. Our value is generally tied to the service we provide rather than the number of employees our customers may have now or in the future. Our model is not reliant on seat-based or user licenses. Four, we help with compliance. Many of our customers use our GLN to help them comply with various laws and regulations. This could include customs filing, security filing, tariff classification, foreign trade zone, warehouse operation, sanction party screening, or public freight rate management. Customers are often wary of taking compliance burdens on solely themselves, especially given the pace of change of regulations and the financial and other consequences of getting it wrong. If we earn our customers' trust to reliably help them comply, then there's not much financial or other benefit to them changing something that's already secure, timely, and accurate. This helps protect our business from new entrants and customers considering taking on compliance burdens themselves. It also helps us grow because when we're a trusted and reliable partner for one compliance initiative, our customers are much more likely to trust us with other initiatives in the future. Five, we're a system of record. For many of our customers, our systems are the official trusted source of truth for their critical supply chain and logistics data. Using the global logistics network, we help them ensure data integrity, accuracy, and security of key supply chain and logistics information that they rely on for daily operations. This makes these customers very reluctant to switch to another provider or to do it themselves. Six, we're supply chain and logistics experts. We're in a very specific market. Our team lives, eats, and breathes supply chain and logistics every day. When changes happen, and they happen quite often these days, you want to know that you're doing business with an expert that's on top of things. Our customers consider this expertise a part of our service offering, and it makes them dedicated Descartes customers. And finally, we're trusted, financially stable, and transparent. We have a very good reputation. We've worked for many years to cultivate trust with our customers through reliable service, fair pricing, secure operations, and continued expansion and innovation. We've built a financially stable business that our customers can be confident will be here for the long term. They have access to our public quarterly financial reports to monitor the strength of the business. All these things protect us from customers considering switching to companies with less operational history, reliability, or financial stability. Our customers value our success. Now to the huge opportunities for Descartes with AI. We're actively investing and delivering results to our customers. Let me hit the three biggest areas of opportunity for Descartes. Our biggest opportunity with AI comes from the data on the Global Logistics Network. Descartes has the largest trove of real-time supply chain and logistics data in the world. We process billions of transactions a year. We have massive amounts of clean historical and real-time information on the sourcing, storage, classification, transportation, tracking, pricing, service history, and financial settlement of most transactions in the global logistics and supply chain markets. AI technologies need data to function. AI technologies are trained and learned from consuming massive amounts of data. AI technologies are only as effective as the data they consume and only as relevant as the timelines, excuse me, of the timeliness of the data they get. Most AI technologies are all trained on the same publicly available information on the Internet. The real value is being able to train AI technologies using non-public information, which is exactly what information we have on our global logistics network. Our global logistics network data is rocket fuel for AI. We'll continue to grow and cultivate data on our GLN in a responsible way that allows our customers to benefit from the collective intelligence of the network. For us, our key focus going forward is respecting, anonymizing, and protecting the data we have while preparing for how our customers may want to use it with newer AI technologies. Secondary, is that we believe AI agents will change the future of who uses our technology. We think it will change both what we sell to some customers and how we support them. We think of an AI agent as a digital coworker trained to do a specific task. AI agents are excellent for reducing human workload by automating repetitive tasks. They're also helpful to handle matters that aren't affordable to have humans do. Historically, our services have been designed to only be used by human workers, often clicking on icons and entering data. However, that isn't the future. We're preparing our services to be used by either human or digital workers. Our customers expect value from digital workers. They don't want inefficiencies in their business from humans doing low-value or repetitive tasks that could otherwise be automated. Rather, they want AI agents performing tasks and supporting humans who are making decisions. We believe this is real and current customer demand for us to meet. I believe our business will shift in three ways. We'll provide customers the ability to get AI agents and digital workers through Descartes. We're already doing this in areas of our business like MacroPoint. In MacroPoint, you can hire a digital worker to call drivers and get a location check on where a shipment is, another digital worker to gather missing shipment documents for you, and yet another worker to address data integrity issues. Each of these AI agents grow our network, improve the quality of the data that GLN has, and reduce costs for our customers. We'll arm human workers with the information they need to make decisions rather than to perform tasks. User interfaces are going to change. Screens with complicated series of clicks and reports will change to specific information needed by human eyes to help make a decision. Finally, services will be designed to be consumable by AI agents. Whether they are Descartes agents for our customers' own AI agents, our services need to be consumable in a machine-to-machine format. This may include providing AI agents access to different types of data in novel ways or enabling AI agents to interact with our own digital workers or technology. We believe AI agents will be part of the future for Descartes and our customers. We're already delivering value to our customers with AI agents and investing in delivering suites of digital workers that can help our customers. In addition to the AI agents I described with MacroPoint, these include natural language searches and AI agents for our Descartes GLN data mine U.S. import business. AI agents to help deal with challenging match scenarios for denied party screening on parties with ambiguous names and addresses. AI agents helping determine free trade eligibility based on past practices, helping our customers reduce their tariff bill. AI agents making automated tariff classification suggestions for goods. And using AI agents to interpret lengthy carrier rate agreements and present optimal selection recommendations. And there's many more. And finally, the third opportunity for Descartes is to make our business more efficient. We run a multinational, multi-currency, multi-pillar business. We operate an enormous distributed technology infrastructure that can be a target for attack from bad actors. We operate and monitor hundreds of products and services at elite availability levels. We provide support to and bill more than 30,000 customers. We grow our business 10% to 15% a year and have historically added three to four new businesses to the Global Logistics Network by acquisition every year. With that footprint, there are opportunities to improve our business with automation. We've invested into AI technologies to do this. Key examples are AI tools helping our software engineers with initial coding, customer support automation to enable customer self-help for routine inquiries, advanced AI technologies to harden our network security posture, and new tools to monitor network performance. We're investing in AI technologies to help our team. We will get more efficient. However, our customers expect that we can reinvest savings generated by AI technologies into further improving the GLN and or reducing our need to hire at the levels we have historically. That seems like a sound approach to me. Overall, AI is a tremendous opportunity for us. We believe it will spur further demand for our trusted, real-time, clean, formatted GLN data and the collective intelligence of the network. It's already allowing us to deliver additional value to our customers with AI agents and it's helping make our business more efficient. We believe that the inter-enterprise scaled network infrastructure of our business puts us in a much better position to benefit from AI than legacy or emerging point or enterprise technology solutions. And finally, to wrap up, Q4 and FY26 were very strong financial results for us. I'm excited about how the business is performing and the opportunity we have in front of us. I'm now going to hand the call over to Alan in the CFO role for the last time on one of these calls as we conclude the year. The good news is that Alan is going to remain a part of our business, and it's a privilege to be able to keep working with him. Ed Gardner will be the new CFO following this call, and he also gets the benefit of Alan's experience and wisdom as we make this transition. Ed Gardner is also on this call and available for investor and analyst questions afterwards, along with both Alan and me. So with that, I'll turn the call over to Alan. to go through the financial results in Mordantown. Alan?