Dassault Systemes Sa Adr

Good morning and thank you for joining our third quarter 2026 earnings conference call and in Dastan in London. I'm with Pascal Daloz, Chief Executive Officer and Chairman of Dassault Systèmes and Rouven Bergmann, Chief Financial Officer to discuss our third quarter 2026 results. Dassault Systèmes results are prepared in accordance with IFRS, the financial figures discussed are well noted. Some of the comments contain forward-looking statements that could differ materially from actual results. Please refer to today's press release in the risk factor section of our 2025 Universal Registration Document and all earnings materials are available on our website. These pre-fabric marks will be available shortly after this call. So now over to you Pascal.

Merci Marie. So good morning to all of you and thank you for being here. and especially for the one who are physically here in the room with us, I always appreciate to have this live interaction with you. I think before Rouven takes you through the number, I really want to start by giving you the big picture. It's about what we are building and why it matters for the next era of the industry. You know, at the start of the year, you remember, We call the 2026 a foundational year. I think it's a year where... How can I say that? It's not a year of waiting, it's a year of building. Building a new category of products, building an AI native platform, developing partnerships, all in services of the growth. And I think for more than 20 years, we have helped industries to connect both worlds, the virtual world and the real world, for them to be able to imagine, design and test. And now, I think at the age of AI, this connection matters more than ever. Why so? Because generating an answer is not enough. You know, you need to create a design, simulated, tested, validated, manufactured, often certified, before it could fly, before it could be on the market. So, whatever is a powerful model you have in your hand, it's not enough. And you need engineering context around it. You need connected environments where AI can be tested against the behavior of the real world. So this is the role of our Gifle twins, our industry world model, to ground AI in science and in the behavior of the physical world. And this is why also 3D experience is evolving into an adjuncting platform, because we are bringing AI together with industrial knowledge and human expertise, so intelligence can move from generating ideas to helping to deliver trusted outcomes. Now, let's start with the performance of Q3. And why this is important? Because I think execution matters. You remember that's what I said last quarter. I think we deliver a strong execution at the high end of our objectives. You saw it, total revenue grew 5%, software revenue grew 5 as well, 3D experience platform plus 25, cloud plus 14%, EPS reached 32 cents, up 8%. But I think, and the revenue growth is now expected to be between 4 to 5%, up from 3 to 5, and the EPS growth is now expected to be between 6 to 7, up from 3 to 6%. So, Alice Global is obviously part of the updated outlook and Rouven will walk through the assumptions and the financial details for you. Behind this number, I think there are some priorities which are driving the progress. The first one is we are helping our existing customers to transform their businesses. and to turn industry pressure into opportunity and you can see the 3DEXPAM platform and the cloud continue to perform strongly this quarter. The most important, we are also winning new customers and this not only in one given industry or in one given geo, it's really broad-based. The second priority is we are taking a major step forward in life sciences. Bringing Artists Global into the portfolio, building towards a unified AI native platform and putting a dedicated leadership in place. And I know it's a topic some of you asked many times how we will integrate all the pieces together and I will come back on this. The third one is we are expanding 3D universe to turn AI advances into practical industrial value. We see this every day in our work with customers. and we will share a few great examples, the one we saw at the Paris Auto Show or the GTC Berlin last week. Just remember one thing, our strategy is very clear, we deliver today and we build for the next wave and we connect the two. This is really the most important and this is what we have done this quarter. And the momentum is really across many many industries, high tech, industrial equipment, automotive. And the next steps for them is not only to serve their local market but is really to industrialize those innovations to bring them to the global market. And we see this similar shift in many industries, robotics, pharmaceuticals, aerospace, where complexity is increasing and the ability to connect the disciplines matter more than ever. Take Comac, the China leading commercial air manufacturers as an example. Their mission is really simple, to bring its first large wide-body aircraft to the global market. And to make this happen, COMAC needs to industrialize the programs, needs to secure the certifications, to get ready to bring the aircraft to the market, and again, both in China, but more importantly, internationally. And that's the key, because the global market needs to be part of the plan from day one. Not something you think after and you put it later. So this is where we enter into the game because with 3DEXPERIENCE platform we bring design, system engineering, simulation, manufacturing, planning in one unified environment, giving everyone a shared view of the program. And with the model-based system engineering, you know, teams can tackle the complexity very early on. and when changes are easier, less costly to make and decisions can have almost no impact. That's how we are helping our customers to manage complex programs and turn the global emissions into the reality. Now, let's look at life sciences. Good news, we start to see some positive signs. You know, Pharma and Biotech are picking up. You have seen like And that's an important point because last time we saw this was at the COVID time. So that's number one. Number two, we are also seeing since improving quarter over quarter at MediData. MediData is flat and again, we come back on this and we expect to return to growth in Q4. Another encouraging sign is that the customers are increasingly seeing the value of our platform approach. We saw this in Q1 with one of the large deals we signed with WCT, Worldwide Clinical Trial. And now we are seeing something similar with NREST. And the idea is very simple. Instead of using different tools for every clinical trial, we give the CRO one AI platform to manage their clinical operations. And the benefits are very clear. The team can work much more efficiently and get better in the diversification industry, which grew at a double-digit rate this quarter. We are searing a very strong momentum, especially in high-tech and around AI infrastructure. You remember we have built expertise in the nuclear a few years ago, which was and still one of the most complex systems to engineer. And I think now we are putting that expertise to work in another equally demand area, which is this AI infrastructure. And because the AI factory is much more than a room full of GPUs, you need compute, networking, power, cooling, Software and all those things need to work together and when you change one element it could affect everything else. So you need to think about the whole systems from day one and to be ready to adapt when things are evolving and you know the cycle of obsolescence is such that things are evolving almost on a monthly basis. First for the data centers and today these different elements are often designed separately. So what do we do with our virtual twin? We bring all of them together, to design them together, to simulate them together, how they will work as one system before anything is deployed. And the goal is very important, is get AI infrastructure up and running faster and reduce the time for the first tokens. That's the motto, reduce the time for the first token. That brings us back to the big picture. because as AI becomes more complex, we can fly against the same system level engineering expertise we have developed in some of the world's most demanding industries. And this is becoming, I would say, on top of the agenda of many of the CEOs who are in this business because for, in general, and now the constraint that all the other industries have to basically be on time, on quality, on cost, On Purpose. You know, we need to apply the techniques we learned from the other industries. And this is where we are stepping in. Now, let's take a little bit of step back and look at the kind of product industries are starting to create. Think about intelligent robots, autonomous vehicles, smart wearables. You know, they are not just traditional products with AI on top of it. They are entirely new kind of products and that changed the way we need to design them. So remember this, most of you are challenges how we are integrating AI in what we do, but the real question is not this one, is how we are evolving to basically support this industry when they are becoming AI native. And with AI native products, intelligence is built from the start. to the physical world and to make sure that it really works. That's what 3D Universe is all about. Connecting intelligence, digital intelligence, to the physical world and it proves and proves that it works. So to do this, we combine industrial know-how with science and AI to create industry world model and virtual twin at scale. In practical terms, what does it mean? It means industry can try out different ideas, see what might happen, test their decision virtually before taking action in the real world. And there is an important point here. In industry, you know, being intelligent is not enough. You also need to trust the results. And that's the reason why we are different. because we bring again science and industrial knowledge into AI so we can explain the results, test them, and validate them. Let me give you two very simple examples. AI can describe very easily an aircraft wings, but cannot design it, and more importantly, cannot certify the wing. It can summarize a clinical trial protocol, but that does not mean it can be validated. So there is a big difference between the formulations and basically or being right are very two different notions. That's why AI needs an environment where it can learn, test against the rules of the real world. And that's exactly what our virtual twins and our industry world model provides. But to this we will come back much more in details at our Capital Market Day. We also are seeing this shift to take place. Remember, in July, we made 3DEXPERIENCE Platform Exantic. We introduced all the virtual companions. You know them, Léo, Marie, Aura, Louis. They have some specific skills. Skills need to work alongside with the people. Now, what do we do? We are exploring with the early AI adopters across industries, across, around the world, you know from AI native startups to some of the big global players and so far you can see on the slides we have around 100 strategic customer partnerships working with us and we are developing industrializing the use cases now the focus is really on taking this early success to the next level and this is what we're going to do in 2027 we plan to scale up Moving from the promising use cases to solutions can be deployed more broadly repeatedly across the customers. Let me give you a more vessel companion. You know, they can take on tasks, work together and bring the right virtual or human experts when it's needed. And the most important thing is they don't just answer the questions. They can actually get work done. drawing on specific skills, industrial knowledge, and they keep the people in control. So here you have a good example, which is what we are doing at Volkswagen. Therefore, companions are helping engineering teams to streamline their workflows and get things done much more efficiently. So we start with Aura as a coordinator. Aura is bringing action plans together, assign tasks, Leo explores the design options based on the Volkswagen's engineering heritage data sets. And they share, Leo shares the results with Aura. And when Aura can review those results, adjust the plans, keep the track in what is happening next. So throughout the process, again, the people remain in control and accountable for the decisions. So the real value is not just automating the task. is getting different skills to work together against industrial knowledge and human overseeing, if you want. It's humanoid robots for Audi with agile robots. Here is an interesting case because this is really the physical AI in action. Leo used our industrial world models to create the virtual twin of the factory. The robots, you know, they have their own virtual twins, too. But what does it mean? It means it allows the teams to explore how the two will behave together in one virtual environment, reflecting the real-world physics. And this is what it gets interested in, assess the cycle times, see how production throughput will be affected. And they can work through these questions virtually before anything goes on the shop floor. And the results, and we have now a lot of KPIs, Fewer unknowns, better informed decisions, much more confidence, and more importantly, the time to deploy. Because at the end, it's how you speed up the commissioning. So, what is in common between the two strengths, between the two examples I shared with you? Is really, we can explore and learn and validate the virtual world before taking illogical AI. And in fact, we are doing the same thinking process, but to a different challenge. And the stakes are especially high because ultimately it's about human health and patient outcomes. So, too often, and now you start to know it, what we learn in the real world does not fly back quickly enough to inform the next scientific discovery. And in fact, losing this gap is one of the biggest opportunities we see in life sciences. and this quarter you know we took an important step in these directions with the completion of the acquisition of ARIS Global. But you know it's much more than adding the drug safety capabilities. It's about connecting the pharmaceutical journey and the formula is released from the molecule to patients to the real world evidence and then bring what we learn platform helping the life science company, biotech, big pharma, to turn those real worlds inside into the next wave of scientific progress. To make this ambition real, we are putting a new operating model in place. In fact, we are bringing together expertise across all the disciplines, clinical developments, discovery, labs, manufacturing, regulation compliance, real-world safety, medical practice also. And we are unifying this capability with our 3DX adjuncting platform. And we streamline the go-to markets. And the goal is very straightforward. It's one coordinated organization, one integrated offer, a simple way for our customers to get access to the solution they need. But to do this, I'm also pleased to welcome Dr. Ashimini Zenous to Dassault Systems. Ash is a physician and a technology entrepreneur and she has a very strong background in digital health and AI. And what makes her background particularly valuable is the fact that she brings together the two worlds, the world of the medicine and the world of the technology. And this is exactly the combination we need to bring our ambitions to life. So our role will be to lead the strategy, bring Medidata, Biovia, ARIS Global together in one AI native platform, connecting molecules, patients, real-world outcomes, and again, closing the loop back to science. She will lead the life science healthcare as one business with a clear focus on the science-driven innovations, the operational excellence, building ever stronger relationships with our customers and partners. So, what about her visions for the business? Now, the mission of this integrated organization is also very simple. You know, the entire purpose of Dassault Systèmes is not to only improve the productivity and to streamline the workflow. It's to inspire smarter treatment for healthier people. And why this is important? Because at the very core of everything we do, you have the patient. Just think about the reach today. Our solution, supporting 15 million patients in a single trial. 16 million patients in the real world. to collect those real-world evidence. And the medicines which are tested, and the COVID vaccine is one of the examples, is used to reach more than 6 billion people on Earth. So that's the dedication of these organizations. We are also covering half of the safety even worldwide across a wide range of therapies and treatment types. And you know, those numbers are telling you a very important story. They show the scale of the ecosystem we are building. And behind the scale, you have more than 5,000 colleagues. Many of them have a PhD, doctors. They are knowledgeable on the therapeutic domains, on the modality of the machines. More than basically only the, you know, the two nodes working closely with the regulators in Europe, in United States, in Asia. We also have built a rich foundation with experience across 42,000 clinical trials and 2 million safety events every year. And why all of this matters? Because when it comes to AI in life sciences, scale and expertise really make the difference They help us to build AI. This is grounded in science and reflects the reality of the industry. So this is an important point, distinction, to keep in mind. In manufacturing, physics sets the rules. In life sciences, biology is the one. And biology is incredibly complex. You can understand by looking at the document. Basically, we can make the process faster or more efficient. It's really about helping the sciences to make new discoveries, advance treatments, and most importantly improve the outcome for patients. So we look forward to share more with you at our Capital Market Day in Paris in November 2017. And before to hand over to Rouven, let me sub up a few messages for you. We are making good progress in our priority, which gives us the confidence in our updated full year objectives. We are also moving from our AI roadmaps to the real-world adoptions and we are taking an interpersonal step forward in licenses. With that, Rouven, the floor is yours.

Thank you Pascal and also a warm welcome to you here. in London joining us in person and also good morning to you online. So as Pascal, as you mentioned, we enter the final stretch of the year with momentum. Our focus remains squarely on the execution of our growth model and on delivering our objectives. The annual run rate is up 6% as your revenue growth is at 5%. and it's at the top end of our objectives that we had set for the quarter. The operating margin expanded by 150 basis points to 31.6% for the quarter. It's well above our objectives and the operating profit was 10% year-on-year. So the strong profitability is converting into cash with cash conversion now reaching 111% year-to-date and this is driving an EPS growth of 8% and an operating cash flow growth of 20% year-to-date. So in short, we are stepping up the execution as we head into the close of the year. Now let's take a deeper look into the financials of the quarter. The total revenue reached 1,552,000,000. It's up 5%. The subscription revenue up 6% in the quarter and year-to-date and licenses growing at 14%. This license growth reflects a robust performance, a strong performance in China and it also compares to a lower baseline versus last year. Highlights to progress in our business model transition. This quarter we are reaching an important milestone because our subscription revenue now represents 50% of our recurring revenue and service revenue was up 7%. and this top line leverage continued and continued operating discipline translated into an uplift of 10% in operating profit with a margin of 31.6% which is up 150 basis points XFX.