Dassault Systemes Sa Adr

Good day and thank you for standing by. Welcome to the SALT Systeme second quarter and half year 2026 earnings presentation. At this time all participants are in listen only mode. After this speaker's presentation there will be the question and answer session. To ask a question during the session you need to press star 1 1 on your telephone keypad. You will then hear an automatic message advising your hand is raised. To withdraw a question please press star 1 and 1 again. Please be advised that today's conference is being recorded. I would like to hand the conference over to our first speaker today, Marie Dumas, Investor Relations Director. Please go ahead.

Good morning and thank you for joining our second quarter 2026 earnings conference call. I'm with Pascal Daloz, Chief Executive Officer and Chairman of Datso Systems and Rouven Bergmann, Chief Financial Officer. They're both on the line with me to discuss our second quarter 2026 results. Datso Systems results are prepared in accordance with IFRS. The financial figures discussed on this conference call are on a non-IFRS basis with revenue growth rates on a constant current speed basis, unless otherwise noted. Some of the comments on this call contain forward-looking statements that could differ materially from actual results. Please refer to today's press release and the risk factors section of our 2025 Universal Registration Document. All learning materials are available on our website and these prepared remarks will be available shortly after this call. I would like now to hand over to Pascal Daloz.

Good morning, everyone. Thank you, Marie, and thank you all for joining us today. Before Rouven will walk through our financial results, I really would like to spend a few minutes on what I believe is a bigger story. This quarter is about much more than the numbers. It's about execution. It's about the steady progress we are making against the strategy we set at the beginning of the year. As you remember, I said From the start, that 2026 will be the foundation year. Not because we expect less, but because we are building for much more. Transforming industry does not happen in a single quarter. It happens customer at a time, one deployment at a time, and one innovation at a time. And this is exactly what this quarter reflects. This quarter, our business performed well. We are reaffirming our full year guidance. Revenue grew 4%, subscription revenue grew twice as fast as the overall business, and earnings per share increased 8%. I think these results reflect the disciplined execution we had. But the numbers, as I was saying, are only a part of the story. The more important story is what we are seeing happening with our customers. Across every major industry we serve, companies are accelerating their digital transformation. They are moving to the cloud, they are preparing their data, and increasingly, they are investing in industrial AI. The conversation has really changed. You know, customers are no longer asking whether AI will transform engineering or manufacturing. They are asking how fast they can deploy it. And this is really an important shift. Why so? Because AI needs context. It needs trusted data, it needs a virtual model of products, factories, operations, and this is exactly what the 3DEXPROM platform was built to provide. This is really why we believe we are uniquely positioned for the next era of the industrial innovation. Now, our strategy remains focused on three priorities for this year. First, helping our existing customers transform. More of the world leading industrial companies are adopting the 3D Expense Platform on the cloud as their digital foundations. They are connecting engineering, manufacturing and operations in a single platform. And they are preparing to deploy AI for virtual twin at the enterprise scale. Second, expanding into new industries. We continue to build the momentum in high-tech, new space and consumer industry. And with the acquisition of ARIES Global, we are significantly strengthening our position in life sciences. Each of those industries expands our opportunity and together they make our business stronger and more resilient. Third, we continue to invest on the platform itself. As AI is becoming the new interface to industrial software, our ambition is not simply to add AI features. It is to build an adjunct platform where the virtual companions become the trusted collaborators for engineers, scientists, and business leaders. In the first quarter, we introduced the architecture behind our vision. This quarter, we are bringing it to life. We are delivering the first adjunct 3D experience platform for the new generation of AI native experiences. And I think we believe this is the beginning of a profound shift in how industrial innovation will happen over the next decade. Now, let me show you what this strategy looks like in practice. Our customers, you know, they operate in a very different industry. They build cars, aircrafts, semiconductors, medicine, consumer products. But today, you know why? All of them are facing the same reality. Complexity is growing faster than ever. Products are becoming smarter. Engineering is becoming more collaborative. Supply chains are more connected. Regulations are more demanding. And now, AI is fundamentally changing how products will be imagined, developed and produced. To take advantage of AI, companies need first to trust the digital foundation they are building. And again, this is exactly what the 3DEXPERIENCE platform provides. It connects data, people, and knowledge. And increasingly, it connects also AI at every stage of the lifecycle. This is why we are seeing a momentum across every industry we serve. In transportation and mobility, manufacturers are accelerating the vehicle developments, while connecting engineering teams across increasingly complex global ecosystems. In aerospace and defense, the well-established leaders are scaling their productions while at the same time the growing new space ecosystem is building its next generation of programs on 3D experience platforms. In high-tech, consumers are managing unprecedented and many more. In life sciences, I think the organizations are bringing together research, development, manufacturing and patient outcomes through the virtual twins and now with the acquisition of ARIS Global, we are taking an important step forward, a unified AI intelligent platform that connects molecules, patients and real-world outcomes. In infrastructures and cities, customers are also using the virtual environment to design, build and operate more resilient and more sustainable infrastructures. And finally, our mainstream innovation business continues to demonstrate the breadth of this opportunity SOLIDWORKS this quarter deliver a growth-based growth across geography with a double-digit unit expansion. Why I took the time to give you this perspective? Because this is reflecting the strengths of the portfolio and our ability to attract the next generation of engineers, designers and innovators. Let me bring this to life with few examples. All those customers I was speaking about They are operating in a very complete different industries, but all of them, they are reaching the same conclusions. To compete in the AI era, they need more than the software, they really need the platform. So, Mahindra expanded its deployments of the 3D Expand platform on the cloud to modernize the product development across its global engineering organizations, but also to build the digital foundations ready to deploy AI-powered virtual twins at the enterprise scale. Their challenge is very simple. They want to reduce the time to market. And the value we bring to them is obvious. It's connecting all the engineering teams across the ecosystems. We are also delivering several important competitive wins this quarter. One of them is the world-leading memory semiconductor manufacturers in Korea Selecting the 3D experience platform to create the digital continuity across the entire product lifecycle. From engineering with CATIA, the product information with Enovia, to manufacturing with Delmere. One platform, one single source of truth from the concept to productions. In consumer industries, we have an interesting case. I didn't know this company, I discovered it. It's very famous in the US. the so-called Polyconcept North America. They are the leader in the personalized goods. And what do they do? They are selecting century to connect the product design and the manufacturing and the consumer experience while they are embedding AI in their innovation process to generate automatically these personalizations. So across every industry we see against the same patterns. Customer are no longer investing simply to improve today's engineering, They are standardizing on 3D Expense Platform and the cloud because building the digital foundation for tomorrow AI-powered enterprise is becoming a must. And they are standardizing on 3D Expense Platform and the cloud because they understand the competitive advantage will come from the data, the knowledge, and the people. AI is an accelerator, but the platform is a foundation. This is what brings me to the next chapter. And today, I'm extremely pleased to announce an important milestone in our life science strategy, the acquisition of ARIS Global. I think you should look at this much more than adding another software company within Dassault Systèmes. It really completes our vision for life sciences. It closes a very important loop between the scientific discovery, the clinical developments and manufacturing, and the real-world patient outcomes. And I think this is creating something the industry has never had before, a continuous intelligent platform powered by AI. So why this matter? Because, you know, life sciences face a remarkable paradox. It's the industry investing the most in research and development, but yet fewer than one in 10 of drugs entering into the development reach the patients. So, the challenge is definitely not the lack of science. It's not the lack of data. It's coming from the fact that the data remains fragmented. Scientific data, clinical data, manufacturing and quality data, safety data, too often, you know why, they live in a separate systems and too often the critical decisions still depend on documents rather than connected intelligence. And AI is a game changer in this case because we can only be a powerful tool as soon as the data is there to support it. This is the reason why this entire industry is moving now towards connected platforms that bring together science, operation and AI in one single environment. This has been our strategy for years. You know it. BioVar for the discovery, Medidata for clinical development, Delmia for manufacturing. And now with ARIS Global, we have the final mission dimension, the real world evidence. Why ARIS Global is so important? Because this company is a leading enterprise platform for pharmacovigilance, Regulatory Affairs and Safety. And it's deeply embedded in the operation of the world's leading pharmaceutical companies and the health authorities, both. To give you an order of magnitude, nearly half of the top 50 pharma companies rely on them. Their software processes approximately 12 million safety cases every year amongst 25 million worldwide, so half of the safety cases are part of their systems. And more importantly, and you will see why this is important, 80% of those safety cases are not public. They are private. So which basically means if you do not get access to it, you will never have the research tools of the real world evidence to train your systems for AI. So this is the reason why This is a very important asset for the entire Dassault Systems strategy. Now, at RIS Global, they have already demonstrated how AI can create tangible value with their AI capabilities. It's already deployed, delivering productivity gains of more than 30%. They are helping customers to identify safety signals faster, to make better informed decisions, And remember, this is not only an automation, it's an intelligent decision support in one of the most highly regulated industry of the world. So what excites us the most is what happened when ARIS Global become part of Dassault Systèmes. Because for the first time, life sciences company will be able to connect every stage of the pharmaceutical life cycle on a single intelligent platform. Again, discovery, clinical development, manufacturing, regulatory compliance, and real-world safety. Every new piece of evidence improves every stage that comes before. For example, the scientific model becomes smarter because you can anticipate some adverse effects. The clinical trials become much more informed because when you test the drug, You already know there are adverse effects. You should do this. Manufacturing becomes more adaptive and patient outcomes continuously improve future innovation. So instead of disconnected systems, the customer again has a lot to gain with the continuous learning loop. And this is what AI needs, not isolated models. but connecting knowledge, trusted data, and continuous feedback. Looking ahead, the acquisition is about much more than expanding our footprint in life sciences. I think it demonstrates the strategy we are executing across Dassault Systèmes, building an intelligent platform where data consistently become knowledge, where AI continuously improves decisions, and where every customer interaction make the platform stronger. And ARIS Global is an important milestone in that journey. But it's also a preview of where all the industries are heading. Because whether our customers design aircrafts, develop medicines, or build factories, the future belongs to the platforms that continuously learn. And this is exactly what we are building. Let me show you how that vision is coming to life through the new AI native solution we are introducing this quarter. If you remember, last quarter, we introduced our AI architecture. This quarter, we put it at work. The 3D external platform is becoming an adjunctive platform, following the Vifold Companion and a new generation of AI native solutions. And this is an important distinction. Why so? Because much of AI Today's AI has been added on top of existing software. You know, a kind of chat box if you want layer over the legacy applications. We took a different approach. We built AI at the core of the platform because industrial AI is fundamentally different. It doesn't just answer to the questions. It has to help engineers to solve their problems, to understand the products, to understand the physics, to understand the scientific model and it understands the context in which all the decisions are made. This is what makes AI useful in mission-critical industry. Now, at the center of this experience, there are the virtual companions. Each is designed for a specific role. You remember, Aura helps the business users to navigate Enterprise Knowledge, and Execute Business Processes. And to give you a concrete example, with OHA project management, a business objective can become an executable project planned up 10 times faster. LEO support engineers as they design, optimize, and validate the complex products. And again, an example, LEO mechanical engineer can begin with an idea, generate high-performance manufacturable designs, while maintaining the full engineering traceability. Marie assists the scientific with the modeling and simulations and scientific decision making across the research life cycle. They are not general purpose assistants, they are really domain experts and each companion understands the language, the objectives and the constraints of the people it works with. So because each One is built on a decade of engineering expertise, scientific knowledge and industry best practice. This is how we are making difference. So now every quarter, those companions, they are becoming more capable. This quarter alone, we introduce more than 11 new industrial competencies and every new competencies transcends every customer using the platform. This is the power of the AI native architecture. Everything I just say is orchestrated by the 3DEXPERIENCE Adjantic platform. The platform provides the governance, the security, the traceability, the digital continuity required for an enterprise-scale AI. And for some of our customers, you know, running on our sovereign AI infrastructure, outscale, It's also an extremely important topic because it's for them the way to retain the complete control of their intellectual property while they are deploying AI in the most critical environment. This matters because many AI systems can retrieve information. Some can generate contents, some can predict the outcomes, But an industrial AI must do something far more demanding. It must generate results that engineers can trust. Results that scientists can validate. Results that manufacturers can certify. And you cannot certify an aircraft engine with an AI that understands only the language. You cannot develop a life-saving therapeutics with an AI that understands text but not biology. Trust comes from understanding how the physical worlds behave and this is why our industry world models are so important. They don't simply learn the pattern from the data. They capture the scientific discipline, the engineering discipline, the industrial knowledge that governs the real world and at the same time they are protecting the intellectual property of our customers. This is what makes industrial AI trustable This is what allow our customers to move from experimentations to enterprise-scale deployment. Now, let me bring all of this to life with one example. And I took one I'm sure you will be very interested with. It's BMW. BMW Group is a very good illustration on how industrial AI look like in practice. Not from a CIO view, from a pure Engineer Vu, the one doing the job on a daily basis. You know, BMW is in designing increasingly sophisticated vehicles under constant pressure to innovate faster, to reduce the cycle time, and to meet even more demanding performance requirements. Take something as familiar than the car door or the central console of the car. Behind what looks very simple, you have components which has been designed with a lot of constraints. Weight, crash performance, stiffness, manufacturability, cost, durability. And the challenge is not to develop only one door or one central console, is really to find the best design among thousands of possible alternatives. And this is where our application and AI work together. Firstly, Using CATIA, today engineers can generate and analyze around 50 high-quality design variants from a parametric model. But you know why? Rather than me telling you the story, let's show how BMW engineers are already applying it. Please launch the video.