PLM news, announcements and industry trends.

Autodesk · Oct 21, 2026

CAD, CAE & Simulation

After an incredible week at Autodesk University 2026, the Forma team showed up with live demos, new sessions, and a handful of things we’d been sitting on for a while and were finally ready to share. Before we get into what’s next, let’s recap what got announced, whether you were there with us in Las Vegas or following along from home. Here are the biggest Autodesk Forma updates announced at AU 2026. 1. Autodesk Forma Street Design Forma Street Design was announced at AU. It brings engineering workflows that start earlier, in the cloud, and across domains, with a credible handoff to Civil 3D. Street design today works backwards: the earliest and most consequential decisions about a corridor, how wide it is, where the lanes go, what happens to the sidewalk, how the intersections resolve, get made in tools that were never built for them, then get rebuilt properly only once the design is largely settled. Rebuilding the design to test one alternative costs real time, and by the time it’s done, there’s often no time left to act on what was learned. In the demo video shown on stage, we showed how Street Design allows for designing streets and intersections in plan and section together, adjusting intersections on the spot, and testing turning radius with swept path analysis powered by Autodesk Vehicle Tracking, and bringing the strongest options into Forma Board. Street Design is currently in development. Join the waitlist for closed beta . 2. Civil 3D is becoming a Forma Connected Client Civil 3D is becoming a Forma Connected Client, joining Revit . The Forma Connected Client designation recognizes when an Autodesk desktop product is deeply integrated with the Forma industry cloud. It enables teams to continue working within detailed design tools they trust, while tapping into the cloud data, context, and workflows from Forma. For Civil 3D, this begins as a tech preview: reference-based interoperability with Forma and Revit, so civil engineers can connect to Forma, reference planning and building designs from within the Civil 3D environment, and carry that context into detailed engineering. Civil 3D information can also contribute back to the shared project context, giving other disciplines more visibility into the civil design. For civil teams, that connection to Forma is the whole point. Early-stage work has historically been stranded. Site context gets assembled, options get tested, analysis gets run, and then detailed design starts and a good deal of that gets rebuilt because the decisions lived in one place and the production work lives in another. Connecting Civil 3D to the Forma industry cloud means the context and the decisions travel with the project instead of being recreated at the handoff. It also means that civil engineers, transportation designers, urban planners, and architects can finally start working against the same cloud data. It’s early days, and it will take time, but we’re working towards a future where civil and architecture share one source of truth instead of exchanging files across a gap. Anyone who has spent a week reconciling a site model against a survey that moved will understand why that matters. Keeping project location consistent is part of the same push. Autodesk is unifying geolocation workflows across Forma, Revit, and Civil 3D, with more products expected to follow. In practice, that means models stop drifting out of alignment between tools, and the manual transforms teams currently do to reconcile them start to disappear. That geolocation work sits inside a broader push to connect infrastructure workflows. Civil 3D data can feed into Forma as a starting point for planning and building design, Forma Street Design lets civil engineers and transportation planners explore street designs earlier before continuing in Civil 3D for detailed engineering, and deeper integrations with Esri ArcGIS and Fugro GeoDin and Ground IQ will bring additional geospatial and subsurface context into these workflows. None of this replaces Civil 3D: it remains the tool for detailed engineering, design, and documentation on infrastructure projects. What’s changing is how early that work can draw on the same context everyone else on the project is already using. 3. Connected design, from site to detailed design Our site-to-detailed-design workflow , got measurably more connected this year. Forma Site Design will soon pull project-specific data directly from Forma Data Management , a DWG import that keeps its layers intact, nothing to convert, nothing to clean up. Reactive modeling in Site Design now allows elements to interact with each other in new ways: draw a road through a building element, and it automatically splits the massing into two, allowing for faster design iterations at the site planning level. From there, the massing models travel downstream to Forma Building Design , where a saved library of preferred apartment types will be able to populate the building directly, and a new interior daylight analysis will measure lux from every window in every room as you design, catching problems while they are still cheap to fix. Once that connection between Forma Site Design and Forma Data Management is live, Civil 3D will be able to see the Site Design proposal, share the same model, and read and write to the same shared project. That lets civil engineers join architects and site planners in developing projects from early on, cutting down on coordination issues, rework, and misunderstandings, and saving everyone time. 4. Forma Board and Forma Data Management At AU, we showed what’s changed since Forma Board was integrated with Forma Data Management: a team can review project information, markup ideas, pull in models, drawings and PDFs, and compare design options and analysis results, all in one shared workspace, with comments, markups, annotations and notes updating in real time for everyone on the project. AI visualization turns a sketch or image into a design rendering in seconds, useful for getting a stakeholder conversation moving before the design is fully resolved. And issues raised in Forma Data Management now surface directly inside Forma Board, so design review feedback and issue tracking stay in the same place instead of living in separate tools. The part worth emphasizing is what happens as the project changes: Forma Board stays connected to Forma Data Management throughout, so decisions stay tied to the model from concept through delivery, and teams stop rebuilding decks to reflect the latest state or wondering which version they’re actually looking at. 5. Autodesk Assistant answers natural-language questions about a project site. In the keynote demo, an architect starting a new site in Forma Site Design asked Autodesk Assistant to pull orthophotos from Esri, gather the surrounding buildings for context, and list what other data providers were available for that location, running all three at once instead of working through them one by one while the context needed came together in the background. The value is in who can ask: site information is usually locked behind either a file or a person. If you want to know something about a site, you open the model, or you find whoever built it and wait. Neither scales, and both mean the question often does not get asked. Junior staff ask fewer questions than they should because each one costs someone else’s time. Decisions get made on assumptions nobody checked. Asking in plain language changes who has access to the project’s own information, which changes how often it actually gets used. 6. Building Layout Explorer Forma Building Layout Explorer generates and compares multifamily layouts against a client’s unit mix. Multifamily layout work is repetitive: a client gives you a unit mix, and you produce options that hit it, balancing circulation, core placement and efficiency. It is days of work, and the number of options you can realistically produce is set by how much time you have rather than by how many are worth seeing. This is early research, not a finished capability, exploring whether a model trained on real building data can help generate and compare layout options faster than doing it by hand. What it produces today is a starting point for exploration, not something to rely on directly, and we’ll share more as the research matures. Try Forma’s design solutions today If you have an active AEC Collection, Revit, or Forma for Buildings subscription, you can log in to Forma Site Design and Forma Building Design with your Autodesk account today. If you don’t, you can get started with the Forma for Buildings bundle . FAQ What did Autodesk Forma announce at Autodesk University 2026? Civil 3D becoming a Forma Connected Client, Autodesk Forma Street Design, deeper site-to-detailed-design connectivity, advances to Forma Board and Forma Data Management, Autodesk Assistant, and Forma Building Layout Explorer. What is a Forma Connected Client? A designation for a desktop product that is deeply integrated with the Forma industry cloud, giving it access to shared data, AI-powered workflows and collaboration. Revit is one. Civil 3D is becoming one. When will Autodesk Forma Street Design be available? Street Design is pre-launch. Join the waitlist for closed beta . Can I watch the AU 2026 sessions I missed? Yes. On demand sessions are available on the Autodesk University website .

Aras · Oct 21, 2026

PLM Software · Customer Stories

ニュース → Aras ® , the next leader in enterprise Product Lifecycle Management (PLM) software, today announced that Parata Systems, the leader in pharmacy automation and medication management solutions, has selected Aras Innovator ® as the PLM backbone to consolidate product development processes for their leading pill dispenser lines, Parata Mini and Parata Max. Integrating to SolidWorks CAD, Microsoft Dynamics ERP, and consolidating and replacing various other systems, Aras will be used for engineering change management, technical publications, supply chain management, quality management and customer service. After an extensive evaluation, Parata chose Aras based on the solution’s extreme flexibility, upgradability, and bidirectional integration capabilities. Working with Aras Gold Certified Partner Razorleaf, Parata will deploy Aras in a multiphase approach, where phase one will include configuration management, change management, supply chain collaboration, and integration to CAD and ERP. “In addition to limited cross-departmental adoption, our existing PLM system, Agile Advantage, had reached end of life and was no longer supported,” said Nathan Naramor, Project Manager of the Aras implementation. “Aras was the clear choice for us because of its flexibility to grow with our business and its easy upgradability. The ultimate goal is to maximize our use of Aras as much as possible to improve interdepartmental visibility of product-related data.” Parata chose Aras based on the following criteria: • Bidirectional integrations to ERP, CAD, and other systems • Ability to easily customize in house • Lower total cost of ownership • “Future-proof” solution able to grow with expanding businessinitiatives “Aras’ forward-thinking approach to PLM is ideal for companies wishing to establish a long-term solution,” said Peter Schroer, CEO of Aras. “We are excited to be an integral part of Parata’s business processes and are proud to help facilitate their future growth.” About Parata Systems, LLC Parata provides technology solutions to support coordinated care, better health outcomes and lower medical costs, empowering pharmacists to grow their businesses. Founded in 2001, the Durham, N.C.-based company is a brand trusted by more than 12,000 pharmacies and a growing number of global health care professionals serving diverse patient populations. Parata’s technology has filled more than 2.5 billion prescriptions, helping pharmacists avoid an estimated 15 million medication errors. For more information, visit www.parata.com About Aras We offer the best Product Lifecycle Management (PLM) software for global businesses with complex products and processes. Resilient PLM platform technology makes Aras more scalable, flexible and upgradable for the world’s largest organizations, and a full set of applications provide complete functionality for the business of engineering. Customers include Airbus, GE, Hitachi, Honda, Kawasaki, Motorola and XEROX. Aras is privately held with global headquarters in Andover, Mass. More at and Twitter @aras_plm

Siemens Digital Industries Software · Oct 21, 2026

Digital Thread & Digital Twin · Manufacturing & MES

Siemens today announced a new collaboration with NVIDIA to develop adaptive AI-powered manufacturing capabilities that combine industrial software, Siemens’ Digital Twin technology and NVIDIA Cosmos to accelerate production planning, inspection preparation and optimization. The collaboration combines Siemens’ Teamcenter® software for product lifecycle management (PLM) and Process Preparation X with NVIDIA Cosmos to create a new AI-powered workflow for PCB inspection, helping electronics manufacturers prepare and optimize automated optical inspection (AOI) processes before physical production begins. The solution is being demonstrated at Siemens Electronics Factory Erlangen, Siemens' Customer Zero for adaptive AI-powered production – with the goal of creating a highly adaptive factory that can rapidly introduce near daily changes (3 to 5 times a week) without disruption. Digital twin of SMT AOI inspection area at Siemens Electronics Factory Erlangen created using the Digital Twin Composer “By connecting the comprehensive Digital Twin to the shop floor and AI we are improving both line productivity and product quality. At the Erlangen factory, we're demonstrating how the deterministic nature of Siemens’ Digital Twin is the foundation for leveraging physics AI for preparing production,” said Mark Hindsbo, executive vice president operations software, Siemens. “By leveraging a ‘virtual’ golden board, electronics manufacturers can change production dramatically quicker without taking the line out of production. This enables electronics manufacturers to respond more quickly to change and helps reach production readiness at scale and speed – while helping to reduce manual engineering effort, accelerate AOI readiness and improve manufacturing quality.” “Manufacturers shouldn’t have to wait for the first physical circuit board to start preparing inspection,” said Rev Lebaredian, vice president, Physical AI Simulation at NVIDIA. “By combining Siemens’ industrial software and manufacturing expertise with NVIDIA Cosmos, we can generate synthetic images of good and defective boards to prepare inspection virtually—helping manufacturers bring new products into production faster.” Image capture of a production PCB and marking of potential defects prior to handoff for manual AOI programming Traditionally, introducing a new product or product variant requires a physical prototype before optical inspection processes can be prepared and trained for quality control. The new approach moves more of that work into the virtual world. Teamcenter provides PCB design, bill of materials and component information while Process Preparation X validates, normalizes and assembles this information into a Digital Twin of the printed circuit board assembly (PCBA). This provides the comprehensive product and manufacturing context needed to prepare inspection before the first physical board reaches the production line. Digital twin modeling of the Golden Board is automated using Process Preparation X software This Digital Twin of the “perfect” reference board, often referred to as the “golden board,” is used with NVIDIA Cosmos to create a fully photorealistic virtual golden board along with synthetic photorealistic samples that represent both good and false conditions created during production. Siemens uses NVIDIA Cosmos Data Factory to generate synthetic circuit-board defect images, enabling inspection-machine calibration before physical boards are produced. 3D component models are included in the Golden Board PCB BOM in Siemens’ Process Preparation X software The workflow then extends into production through an AI-supported secondary inspection capability. When an AOI system identifies a potential defect, the secondary inspection capability can help analyze the detected condition and determine whether it represents a real defect or a false call. Together, these technologies are intended to create an end-to-end digital and AI-supported workflow spanning virtual AOI preparation, calibration and inspection support once production begins. Images of the Golden Board and potential defects are automatically produced from the digital twin of the PCB using Process Preparation X software and NVIDIA Cosmos Customer Zero at Siemens Electronics Factory, Erlangen The collaboration is being demonstrated at the Siemens Electronics Factory in Erlangen, Germany, which serves as Customer Zero for broader Siemens and NVIDIA work on adaptive, autonomous and AI-powered production. The factory contributes the manufacturing use case, production environment and subject-matter expertise in a highly adaptable, high-mix environment with ever-changing production processes. The project is targeting up to 85 percent less manual engineering effort in AOI program generation – with 40 percent fewer false calls and more than 5 percent first-pass yield improvement. For manufacturers, the intended value is earlier inspection preparation, reduced manual engineering effort, lower dependency on physical samples, better-calibrated inspection processes, reduced manual verification, faster production readiness and improved manufacturing quality. The collaboration also demonstrates how the Digital Twin and industrial AI can connect engineering intent with real-world inspection across the digital and real stages of production and how the comprehensive Digital Twin evolves from a virtual representation of the product into an active foundation for AI-powered manufacturing and quality processes. To learn more about how Siemens is exploring the future of physical AI with Process Preparation X, visit https://www.siemens.com/en-us/products/tecnomatix/process-preparation-software/ Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens' software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today's ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation. Siemens Digital Industries (DI) empowers companies of all sizes within the process and discrete manufacturing industries to accelerate their digital and sustainability transformation across the entire value chain. Siemens’ cutting-edge automation and software portfolio revolutionizes the design, realization and optimization of products and production. And with Siemens Xcelerator – the open digital business platform – this process is made even easier, faster, and scalable. Together with our partners and ecosystem, Siemens Digital Industries enables customers to become a Digital Enterprise. Siemens Digital Industries has a workforce of around 70,000 people worldwide. Siemens AG (Berlin and Munich) is a leading technology company focused on industry, infrastructure, mobility, and healthcare. The company’s purpose is to create technology to transform the everyday, for everyone. By combining the real and the digital worlds, Siemens empowers customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more livable, and transportation more sustainable. A leader in industrial AI, Siemens leverages its deep domain know-how to apply AI – including generative AI – to real-world applications, making AI accessible and impactful for customers across diverse industries. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. In fiscal 2025, which ended on September 30, 2025, the Siemens Group generated revenue of €78.9 billion and net income of €10.4 billion. As of September 30, 2025, the company employed around 318,000 people worldwide on the basis of continuing operations. Further information is available on the Internet at www.siemens.com . Note: A list of relevant Siemens trademarks can be found here . Other trademarks belong to their respective owners. Contacts for Press Siemens Digital Industries Software PR Team Email: press.software.sisw@siemens.com Further Reading Press Release all Siemens expands agentic AI collaboration to tackle 3D IC power and thermal challenges with NVIDIA October 21, 2026 Press Release all Siemens extends next-generation data acquisition portfolio for modern physical testing with Simcenter SCADAS ES October 15, 2026 Press Release all Siemens transforms computational lithography for next-generation AI semiconductors with NVIDIA cuLitho October 14, 2026

NVIDIA Newsroom · Oct 21, 2026

AI in Product Development · Digital Thread & Digital Twin

October 21, 2026 by James McKenna To increase productivity, accelerate innovation and create new opportunities for economic growth, Europe’s industrial leaders are tapping NVIDIA CUDA-X and Omniverse libraries , as well as NVIDIA Cosmos and Nemotron open models, across the engineering and manufacturing lifecycle. Bringing those capabilities together on the factory floor, the NVIDIA Factory Operations Blueprint (FOX) is now available in early access on GitHub , providing a path for developers to build and deploy always-on factory manager agents that coordinate work across machines, and specialist agents focused on use cases such as visual inspection, root cause analysis, report generation and fleet management. These agents can connect to frontier models, including NVIDIA Cosmos and Nemotron, and foundational knowledge including computer-aided design, product lifecycle management, simulation software, and IT and operational technology systems. Building on the FOX blueprint enables the ecosystem to build industrial agents that monitor factory operations in real time, correlate visual findings and machine logs to investigate problems, and coordinate responses. With Cosmos in-context learning, these agents can adapt using examples and documentation, without fine-tuning. Siemens is integrating agents built using FOX into Intelligence Center X, its agentic enterprise system, to help identify root causes and coordinate factory operations, for estimated cost savings of 20%. Siemens’ Process Preparation X agents use NVIDIA Cosmos Data Factory tools and skills to generate defect images for circuit-board inspection. At its Erlangen electronics factory, Siemens used this approach to reduce calibration time of inspection processes by 40%. Read All NVIDIA in Europe Announcements Accelerating Engineering From the First Design NVIDIA CUDA-X libraries accelerate simulation, giving engineers faster feedback to explore design alternatives before committing to physical prototypes. At GTC Berlin, NVIDIA is releasing cuDSS for general availability , accelerating the demanding calculations behind the complex simulations of structural stress, fluid flow, electromagnetic fields and semiconductor device behavior. Mercedes-Benz uses Siemens’ Simcenter STAR-CCM+ running on NVIDIA GPUs to compare vehicle designs through faster aerodynamic simulations. In factories, the company applies NVIDIA Cosmos 3 and the Metropolis VSS blueprint to build agents for vehicle inspection and windshield assembly verification, detecting deviations and ensuring consistently high quality. Cadence is benchmarking automotive fluid-dynamics workloads on NVIDIA GPUs to assess opportunities for faster design iteration, while Autodesk brings the NVIDIA PhysicsNeMo and Warp frameworks into NavPack, integrated with Autodesk Alias, to help designers like those at Bentley’s predict vehicle performance and iterate faster using simulation-trained AI. imec and Infineon are accelerating semiconductor simulation with Synopsys software and NVIDIA accelerated computing. Benchmarks of S-Litho on imec’s model show up to 40x speedup, while Infineon uses Sentaurus Device with NVIDIA CUDA libraries to achieve a 12x speedup in 3D breakdown simulation for next-generation power devices. Leonardo is working with Neural Concept on AI-driven aircraft design, tapping NVIDIA Omniverse libraries to bring aircraft geometry and aerodynamic simulation results together. Video courtesy of Neural Concept and Leonardo. Car manufacturers use Dassault Systèmes ’ LEO & AURA Virtual Companions, powered by NVIDIA Nemotron, and orchestrating CATIA AI Agents & SIMULIA AI Agents with SIMULIA solvers, accelerated by NVIDIA CUDA-X libraries, to rapidly design and virtually test vehicle doors before building physical prototypes. UniversalAGI is working with Saab to protect sailors from life-threatending wave impacts at sea, using UniversalAGI’s Latent Interaction Field Transformer physics AI foundation models with accelerated processing by NVIDIA Warp to evaluate thousands of wave conditions and headings in real time instead of days. These capabilities enable vessels to anticipate dangerous motion and autonomously select safer routes to avoid putting sailors at risk, while NVIDIA Omniverse libraries help visualize vessel behavior at sea. PhysicsX is working with GB1, Britain’s America’s Cup team , on AI-powered yacht design, using NVIDIA Omniverse libraries to visualize hull geometry and pressure fields, while Fermioniq ’s Sofia agent helps Deltares and Dutch ministries model river flow by translating natural-language requests into simulation solvers built on NVIDIA Warp. Mistral plans to use CUDA-X, Warp and Omniverse libraries to accelerate physics simulation and generate training data for engineering AI models. Testing Factories and Robots Virtually NVIDIA Omniverse and Isaac libraries enable digital twins and robotics simulation , helping manufacturers evaluate production systems and robot behavior before making changes on the factory floor. Video courtesy of Agile Robots, Audi and Dassault Systèmes. Audi uses a battery-production digital twin built with NVIDIA Omniverse libraries to test production concepts before physical equipment is available. The company is also working with Dassault Systèmes and Agile Robots to simulate and validate mobile humanoid robots and logistics processes in DELMIA virtual twins built with Omniverse libraries. Schaeffler is integrating digital twins of its humanoid actuators — components that move robot joints — with NVIDIA Isaac simulation frameworks, helping its robotics partners evaluate component behavior in simulation and reduce physical testing. Wandelbots uses NOVA with Isaac Sim , built on Omniverse and OpenUSD, to validate assembly workflows digitally and prepare applications for physical robots. Falkor , a KONGSBERG company, is integrating agents and NVIDIA Omniverse libraries into its platform. With Microsoft Azure NCv6 virtual machines powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, agents with spatial intelligence combine engineering data, operating information and 3D equipment geometry to see, simulate and reason over industrial facilities, supporting inspection and maintenance use cases across the process and energy industries. Video courtesy of Falkor, a KONGSBERG company. The same simulation approach extends to AI infrastructure. Schneider Electric , together with AVEVA , is developing an AI factory digital twin built for Nscale with Omniverse libraries to help engineers compare power, cooling and equipment configurations before making physical changes. Read All NVIDIA in Europe Announcements

NVIDIA Newsroom · Oct 21, 2026

CAD, CAE & Simulation

October 21, 2026 by Timothy Costa NVIDIA cuDSS, launched today in general availability, is an NVIDIA CUDA-X library that shifts a critical part of engineering and scientific simulations onto GPUs. Complex engineering feats — from advanced chips to aircraft to industrial systems —– depend on simulation to predict how designs will perform before they’re built. As designs become more complex, simulations take longer to run, increasing computing demands and limiting the number of ideas teams can test. NVIDIA cuDSS accelerates solving large sparse systems — the math-heavy step that repeats throughout engineering and scientific simulations and often limits how many designs a team can test — by tapping into GPUs. This lets applications reduce time to results and increase simulation throughput, enabling end users to explore larger more realistic models, more accurate representations and a wider design space at a reduced cost. Read All NVIDIA in Europe Announcements Industry leaders are already integrating NVIDIA cuDSS into their tools to transform workflows for their end customers: • Applied Materials: With cuDSS, Applied Materials accelerated sparse solves in Ginestra, its physics-based, defect-centric simulation platform, by up to 10x versus CPU-only approaches, speeding exploration of semiconductor materials and device reliability. • COMSOL: Using COMSOL Multiphysics with NVIDIA cuDSS on NVIDIA accelerated computing, ZEISS SMT achieved a measured 4x speedup in an EUV lithography plasma simulation benchmark, completing the full simulation in six days and 14 hours. • Cadence : Uses cuDSS in its Celsius software to accelerate simulations of heat, structural stress and warpage in advanced chips and 3D chip designs, helping customers including Nokia analyze complex designs faster. • Dassault Systèmes : Dassault Systèmes reports an 8-10x speedup using cuDSS within its Abaqus structural and computer simulation technology electromagnetic solvers for performance-critical simulations across industries. • Siemens : Siemens uses cuDSS in Simcenter STAR-CCM+ to accelerate simulations of structures in fluid-structure interaction workflows. • Synopsys : Synopsys uses cuDSS in its Sentaurus Device simulator tool to accelerate simulations of advanced logic, memory and power chips, helping Infineon model increasingly complex devices faster. Accelerated Simulation at Scale As simulation workloads grow in size and complexity, cuDSS can scale out of the box from individual workstations to large computing clusters. Developers can integrate cuDSS into existing engineering, scientific and optimization software offerings, bringing GPU acceleration to tools already used by engineers and researchers. They can use cuDSS through C, C++, Python and Fortran interfaces. Featured image courtesy of Dassault Systèmes. Read All NVIDIA in Europe Announcements

NVIDIA Newsroom · Oct 21, 2026

CAD, CAE & Simulation · AI in Product Development

News Summary: • Agile Robots announces Agile ONE SN, a humanoid reference platform built on NVIDIA Isaac GR00T and NVIDIA Jetson Thor with planned integration of the NVIDIA Halos safety stack, giving developers and researchers a complete platform for frontier humanoid development and validation. • NVIDIA Isaac platform adds new Isaac OS runtime, Isaac Lab 3.0 for robot learning and open source reference workflows that combine ChatGPT Astra with NVIDIA Isaac and Cosmos on NVIDIA DGX Station to accelerate robot data generation, training and evaluation. • Europe’s robotics ecosystem, including leaders like ABB Robotics, Flexion, Hexagon Robotics, Humanoid, KUKA, NEURA Robotics, Sereact and Universal Robots, is building on NVIDIA’s open robotics platform to advance autonomous robotic systems. GTC Berlin— NVIDIA today announced a major expansion of the NVIDIA Isaac™ open robotics development platform, adding a reference humanoid, datasets and runtime, along with updates to its robot learning framework. Together, these technologies give developers an integrated path from data to deployment to bring general-purpose robots to the real-world faster. “General-purpose robotics is advancing at extraordinary speed — the ChatGPT moment for robotics is fast approaching,” said Jensen Huang, founder and CEO of NVIDIA. “With NVIDIA Isaac, our full-stack open robotics platform, we are giving developers the models, data, simulation tools and computing systems to bring physical AI to the world’s largest industries.” Agile ONE SN — A Frontier Humanoid Reference Platform for Research Agile Robots announced Agile ONE SN, a full-stack reference humanoid preconfigured with NVIDIA Isaac GR00T and powered by the NVIDIA Jetson Thor ™ edge AI platform. Robotics researchers and developers often struggle to integrate the separate simulation, training and deployment tools needed to take robot skills from development to the physical world. Agile ONE SN unifies these tools and workflows on a single platform, cutting setup time from weeks to days. Agile ONE SN features: • Agile ONE S humanoid body, standing 1.40 meters (4.59 feet tall) and weighing 40 kilograms (88 pounds), with 31 degrees of freedom and a 3-kilogram payload per arm. • Dual WUJI Hand 2 five-finger hands, each offering 20 degrees of freedom, 1kHz force control and tactile sensing for dexterous manipulation • NVIDIA Jetson Thor onboard compute, delivering 2,070 FP4 teraflops of compute and 128GB of unified memory for onboard reasoning and real-time control. • Multimodal sensing, including head and wrist cameras, lidar sensors and inertial measurement units to help the robot perceive and interact with its surroundings. • NVIDIA Isaac GR00T platform, which comprises the GR00T foundation model, a full workflow from data capture to deployment and whole-body control for coordinated movement. • Safety capabilities, with planned integration of on-robot safety protocols with the NVIDIA Halos full-stack safety system for robotics. “The breakthrough for humanoids is usefulness at scale: one adaptable machine that learns many skills and works across environments built for people,” said Zhaopeng Chen, founder and CEO of Agile Robots. “Today, Agile Robots introduced Agile ONE SN, a full-stack, open reference humanoid built on NVIDIA Isaac GR00T, powered by Jetson Thor, giving developers a ready system to create, train and validate the capabilities needed to put humanoids to work.” Scheduled to be available in early 2027, Agile ONE SN is designed to advance frontier humanoid research and development, with interest from leading institutions such as Stanford University and ETH Zurich. New NVIDIA Technology Accelerate Robot Learning — From Data to Deployment New NVIDIA technologies help developers generate training data, scale robot learning, build simulation environments and deploy robot applications, reducing time to solution. • SimReady Central Library offers more than 2,000 simulation-ready robotics and factory assets, while the Human 1M Dataset includes diverse human activities, synchronized three-view video and 3D hand tracking. Both are available on Hugging Face, with the full Human 1M Dataset accessible to Cosmos Coalition partners. • NVIDIA Isaac Lab 3.0 is now generally available, providing an open source, agent-ready learning framework training and refining robot policies from desktop to data center using multi-GPU workflows and physics engines including Newton and NVIDIA PhysX ™. • NVIDIA Isaac OS, an open source runtime framework built on NVIDIA Holoscan, connects AI, sensors, controls and robot hardware for deterministic, real-time performance from simulation to physical robots. • NVIDIA is releasing three open source reference workflows for NVIDIA DGX Station ™ that help developers refine GR00T 1.7 models with reinforcement learning, train dexterous manipulation skills in NVIDIA Isaac Lab and post-train NVIDIA Cosmos ™ world models for synthetic data generation using ChatGPT Astra. • NVIDIA Halos provides a full-stack safety platform built on NVIDIA IGX Thor ™ to help move autonomous vehicles and robots from development to safer real-world deployment at scale. SimReady Central Library on Hugging Face includes simulation-ready assets from Comau, FANUC, ForgeCAD, Franka Robotics, LG Electronics, Palatial, Robotiq, Standard Bots, Universal Robots and Vertiv. NVIDIA’s open source robot learning reference workflows running on DGX Station will be supported by solutions from 1X, Agile Robots, Humanoid, Maven Robotics, Mentee, MicroAGI, Rhoda, Sereact and Skild AI. Robotics partners are integrating NVIDIA Halos within their safety architectures, including Agile Robots, Agility, Dexory, FORT Robotics and KION for warehouse robot safety. SICK is evaluating Halos to advance dynamic safety for industrial robots, helping them respond to changing conditions beyond fixed zones. Advantech and NexCOBOT are developing NVIDIA IGX-based industrial computing platforms, while Reynolds & Moore, Accenture and TÜV SÜD are accelerating the certification process for the safe deployment of industrial physical AI. European Robotics Leaders Build on NVIDIA Open Technologies ABB Robotics is integrating NVIDIA Omniverse™ libraries with RobotStudio HyperReality to simulate and virtually commission robotic systems before installation across a wide range of industries. KUKA Group is using Omniverse libraries in its Visual Components FactoryLens to add real-time rendering for factory simulation workflows. Sereact is using Isaac GR00T with Cosmos Transfer to train and post-train its Cortex platform for goods handling, running on Universal Robots industrial robot arms. Using Jetson Thor, Holoscan Sensor Bridge, Halos and NVIDIA Isaac OS, Humanoid is developing and validating robot behaviors before industrial pilots, Hexagon Robotics is building AEON perception capabilities including teleoperation video, localization, mapping and object pose detection, and Bear Robotics is accelerating KR1 perception and model execution while post-training Isaac GR00T through Kino-2 for warehouse, returns and industrial workflows. Toyota Material Handling Europe and SoftServe use Omniverse and the NVIDIA Isaac Sim™ framework to develop autonomous material-handling solutions, reducing deployment time by up to 50%. Idealworks is using NVIDIA open models and Jetson to develop memory capabilities for autonomous mobile robots in factories. Analog Devices Inc. (ADI) is using NVIDIA Jetson AGX Orin™ and the cuVSLAM library to build autonomous mobile robots. Hugging Face and Pollen Robotics are using NVIDIA Isaac Lab, Isaac Sim and Newton to build Microduck, a small, open source robot designed to help developers and students train, modify and share new physical behaviors. Franka Robotics is using NVIDIA NIM™ microservices and Isaac Sim to turn natural-language requests into actions on its Franka FR3 Duo robot system. Bellboy is using Jetson and GR00T to power Billie, its first robot teammate built on Bellboy’s physical AI infrastructure, already deployed at hotels operated by Marriott, Hilton, IHG and OYO. Flexion is using Isaac Lab and Newton to scale learning for Reflect, its unified robot intelligence model combining reasoning and whole-body movement for complex manipulation across robot bodies. Schaeffler is contributing its actuator technology as an exclusive foundational component for Newton, helping accelerate humanoid robot development with NVIDIA Isaac Lab and Isaac Sim. NEURA Robotics is using Isaac Sim and Isaac Lab to generate synthetic training data and train robot skills in simulation for Neuron AI, its multimodal foundation model. Watch the GTC Berlin keynote from Huang and explore sessions . About NVIDIA NVIDIA (NASDAQ: NVDA) is the world leader in AI and accelerated computing. Certain statements in this press release including, but not limited to, statements as to: expectations with respect to growth, performance, availability, and benefits of NVIDIA’s products, services and technologies, and related trends and drivers; expectations with respect to NVIDIA’s third party arrangements, including with its collaborators and partners; expectations with respect to technology developments, and related trends and drivers; expectations with respect to advances in general-purpose robotics and the adoption and deployment of physical AI across industries; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the “safe harbor” created by those sections based on management’s beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; NVIDIA’s reliance on third parties to manufacture, assemble, package and test NVIDIA’s products; the impact of technological development and competition; development of new products and technologies or enhancements to NVIDIA’s existing products and technologies; market acceptance of NVIDIA’s products or NVIDIA’s partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of NVIDIA’s products or technologies when integrated into systems; NVIDIA’s ability to realize the potential benefits of business investments or acquisitions; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its Annual Report on Form 10-K and Quarterly Reports on Form 10-Q. Copies of reports filed with the SEC are posted on NVIDIA’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. The statements above are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described for our products is subject to change and remains at the sole discretion of NVIDIA. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein.

nec.com · Oct 20, 2026

Digital Thread & Digital Twin

Tokyo, October 14, 2026 \- NEC Corporation (NEC; TSE: 6701) has announced its participation in the upcoming 32nd ITS World Congress as an exhibitor at the ITS Japan Pavilion, which will showcase public- and private-sector exhibitors affiliated with ITS Japan. Additionally, NEC will present a technical paper during a session at the event. This five-day conference and exhibition, which brings together experts from industry, government, and academia, focuses on intelligent transport systems (ITS) and will be held in Gangneung, Korea, from October 19 (Monday) to October 23 (Friday), 2026 (ITS Japan Pavilion; Booth number: S25). The ITS World Congress, which aims to solve road traffic problems through the widespread adoption of ITS and to create business opportunities, is the only world congress co-organized annually by three key ITS organizations from around the globe (Europe: ERTICO; the Americas: ITS America; the Asia-Pacific region: ITS Asia-Pacific). This event features a vast variety of technical sessions, exhibits, panel discussions and more, serving as a forum for participants to exchange information from diverse perspectives on a wide range of topics, including technological development and market trends. Figure 1. The concept of physical AI-enabling digital twin cycles Exhibit Overview Based on the concept of “Shaping the Mobility of Tomorrow,” NEC aims to harness ICT and utilize road infrastructure and related data to create new environmentally friendly mobility services that ensure safety, security, and convenience. Moreover, the data collected through these services will be integrated and used to facilitate smarter, more sustainable and efficient transportation. At this year’s ITS World Congress, NEC will use domestic and global case studies, videos, and panels to introduce a solution that combines three core technologies to achieve next-generation traffic control integrated with physical AI on expressways: (1) digital twin data-fusion technology, (2) wrong-way driving detection utilizing fiber-optic sensing, and (3) individual vehicle monitoring technology using long-range LiDAR for driving safety support. These technologies will advance the evolution of social infrastructure, including distributed edge AI processing platforms, by enabling spatiotemporal synchronization of sensor data collected across wide areas using fiber-optic sensing. Figure 2. Data fusion technology for integrating multimodal sensor data (1) Road State Monitoring with Multimodal Sensing On a digital twin platform with real-time spatiotemporal synchronization, data fusion technology enables both real-time, detailed monitoring of road environments and the intelligent transformation of regional road networks. (2) Wrong-way Driving Detection Using Distributed Fiber-Optic Sensing By utilizing continuous sensing along an entire route, monitoring blind spots can be eliminated. This allows for the collection of early warning information, enabling early detection of unforeseen circumstances, such as vehicles traveling in the wrong direction or obstacles on the road. In addition, traffic flow and safety are improved by alerting following vehicles to take evasive action. (3) LiDAR Sensing Technology for Driving Safety Support on Expressways Capable of capturing 3D expressway information in real time, LiDAR technology is used to detect and track moving vehicles while quickly providing traffic condition updates to autonomous and other vehicles, thus promoting safe driving. The results of field trials conducted on expressways will also be presented during the poster presentation and at the booth exhibit featuring the accepted technical paper. Going forward, NEC will leverage its ICT infrastructure technology and proprietary analytical AI to deliver digital infrastructure for the AI Transformation (AX\) era. This infrastructure will enable the deployment of physical AI across regional road networks through digital twins that are spatiotemporally synchronized in real time. Note: • (\) AI Transformation (AX) refers to initiatives that go beyond simply implementing AI as a tool. Instead, AX involves reshaping business processes, business models, and organizational culture based on AI. About NEC The NEC Group leverages technology to create social value and promote a more sustainable world where everyone has the chance to reach their full potential. NEC Corporation was established in 1899. Today, the NEC Group’s approximately 110,000 employees utilize world-leading AI, security, and communications technologies to solve the most pressing needs of customers and society. For more information, please visit https://www.nec.com, and follow us on LinkedIn and YouTube. NEC is a registered trademark of NEC Corporation. All Rights Reserved. Other product or service marks mentioned herein are the trademarks of their respective owners. ©2026 NEC Corporation. Press "×" (Close) or Esc key Press Esc key for close

capgemini.com · Oct 20, 2026

PLM Software · AI in Product Development

Product innovation is becoming faster, more connected, and more complex. Yet for many organizations, the data and processes that support the product lifecycle remain fragmented, difficult to trace, and hard to scale. Capgemini’s new POV explores how AI can help change that. It looks beyond the hype around the technology to focus on where AI can create tangible value in PLM, from strengthening digital continuity and improving BOM quality to supporting collaborative engineering, software-hardware synchronization, and model-based systems engineering. The point-of-view also introduces a practical view of human-AI collaboration in PLM, showing how intelligent agents and engineering teams can work together to simplify complexity, accelerate decisions, and improve lifecycle outcomes. If you are looking to understand where AI can make a real difference in your PLM landscape, and how to move from potential to measurable impact, this POV is a strong place to start. Read the POV to explore how Capgemini can help organizations shape the next generation of AI-powered PLM. Click to read AI-powered PLM

openbom.com · Oct 20, 2026

PLM Software · AI in Product Development

Every day brings another example of AI at work: reading documents, writing code, analyzing data, generating content, or helping someone complete a task through conversation. Teams are evaluating what AI can do across the applications they use and asking where it can make their own work easier and more effective. Engineering and manufacturing teams are asking the same questions. Their work involves specialized information, several disciplines, different software tools, and decisions that affect a physical product. They want help bringing information in, understanding what it means, finding problems, and deciding what to do next. At OpenBOM, our AI strategy is to make your work smarter in many practical ways. AI can help import product information from engineering documents, analyze BOM data, support decisions, and change how you interact with the information. Conversation becomes another way to ask questions, investigate a finding, and prepare a useful result alongside the product views and tables you already use. We are bringing individual AI features into OpenBOM while also developing focused functions and workflows that help you get a specific job done. Build readiness is one example: bring the relevant information together, determine what is missing or inconsistent, and help the team prepare for its next build. This connects AI for product lifecycle management to a concrete engineering and manufacturing outcome. From AI features to getting a job done An AI feature can make an individual step easier. Drawing import reduces transcription work. BOM analysis surfaces observations that deserve attention. A conversational assistant helps people explore information and prepare reports. Each can be useful in everyday work. We also want to connect these capabilities around the jobs teams need to complete. A build-readiness workflow brings product information, quantity calculations, validation, and review into one process. Its result is an understandable assessment of what supports the planned build and what needs action. The distinction matters because teams often have the information but still spend considerable effort assembling an answer. Engineering has the design, purchasing has sourcing records, and manufacturing has the build requirement. Someone must connect those contributions and resolve the gaps. OpenBOM’s product model provides the shared context. Software performs explicit calculations and checks. AI helps interpret incoming information, investigate findings, and explain the results. People resolve ambiguity and make consequential decisions. Together, these capabilities can help a team complete a defined job with less preparation and clearer evidence. AI can help create the product data foundation Connected product data gives AI useful context. But getting information into that foundation is often the first obstacle. A supplier sends a PDF drawing. Another supplier sends an Excel workbook with unfamiliar column names. A design partner delivers a package containing CAD files, drawings, and STEP derivatives. The information exists, but turning it into manageable product data requires preparation. This is why AI-assisted import matters. It can reduce the effort required to recognize what incoming information represents and prepare it for the product model. OpenBOM’s AI-powered PDF drawing import provides an early example. A drawing can contain a part number in its title block and component information in a BOM table. OpenBOM can recognize that information and turn it into structured Items and BOM data. The October 2026 update describes the preview and confirmation workflow. Users review the recognized information before importing it. The PDF importer is currently in private beta and requires an AI license. Once accepted, the information can participate in the normal product workflow: reviewing a BOM, adding properties, attaching supporting files, managing revisions, and preparing downstream work. That is the connection between document understanding and PLM. From Excel and file packages to connected product information PDF drawing import is the first step in a broader AI-powered ingestion strategy we are developing. The direction is to accommodate the different ways engineering and manufacturing information arrives, including spreadsheets, supplier documents, legacy exports, and packages of related files. For an unfamiliar Excel workbook, AI could help identify relevant tables, interpret column names, recognize quantities and units, and propose a product hierarchy. The proposed mapping would establish which values belong to reusable Items and which describe their use in an assembly. For a file package, AI could help classify the contents and propose connections between the engineering definition, CAD sources, drawings, and derivatives. Supported CAD extraction would remain important for establishing actual design dependencies. File names alone do not establish an assembly structure or an engineering identity. Our goal is to progressively reduce manual preparation and mapping. Universal AI-powered import remains a development direction; different formats and sources will require different levels of support and review. I believe the import experience should preserve the original sources, make proposed mappings inspectable, and ask specific questions when identity, revision, or structure is ambiguous. An unresolved match should remain visible until the team has enough evidence to accept it. The product model gives imported information engineering meaning Recognizing information is one part of the work. Establishing what it means in the product is the next. OpenBOM’s flexible data model lets companies organize Items, properties, catalogs, product structures, and relationships according to their needs. The product knowledge graph connects these records so engineering and downstream teams can navigate their context. Our Engineering Frontend architecture brings design sources together with a reviewed engineering definition. This matters for products that combine mechanical, electrical, electronics, and software contributions, often developed in several tools and companies. A few distinctions are essential. A Design Folder organizes managed files and versions. A Design BOM describes captured design dependencies. An Engineering BOM describes engineering Items and their uses in a product. An Item’s identity differs from its quantity in a particular assembly. As explained in Design BOM vs Engineering BOM , a CAD file can represent multiple configured Items, and several design representations can describe one Item. Drawings and software releases carry their own source context. AI-assisted import should preserve these meanings. The model supplies a place to organize the recognized information; confirmed relationships make it useful for engineering work. A connection records context, while review establishes whether that context is appropriate for the product. Combine repeatable checks with AI BOM Health Once the product information is organized, the team can review it. OpenBOM BOM Review combines deterministic check cards with AI-powered General BOM Health. Deterministic means the software evaluates an explicit rule consistently. Examples include required-property validation, part-number rules, structural loops, and quantity consistency with reference designators. AI BOM Health adds a different kind of review. It can surface similar parts, unusually long lead times, missing data patterns, and other observations that deserve attention even when a fixed rule has not been violated. AI BOM Health findings are informational insights. They are not presented as Errors or Warnings. A reviewer decides which observation deserves investigation, a comment, or an assigned task. The card is available with an AI subscription add-on. These layers work together. Explicit rules establish whether defined conditions are satisfied. AI helps identify concerns that require interpretation. Engineers and other responsible people decide what the findings mean for the next business event. Review can also improve the process. If the same AI observation repeatedly reveals a meaningful problem, the team can decide whether it should become a defined requirement and a repeatable check. This is one way experience from one review can improve the next. From BOM Review to build readiness BOM Review can also provide a starting point for a broader question: what is missing or inconsistent for a planned build? Build readiness is one of the focused jobs we want to help teams complete by combining import, product context, validation, and AI assistance. We are exploring this direction under the working name Launch Master, connecting a specific build commitment with the information that supports it. Launch Master remains a proposed application direction. The approach brings incoming information into OpenBOM’s connected product model, uses deterministic calculations and checks to evaluate defined conditions, and uses AI to explain the findings and support investigation. Over time, it can extend review from the product definition to dependencies across the teams preparing the build. Each finding should expose its evidence and the action or decision it requires. Consider an illustrative ten-unit build that requires twenty sensors when only sixteen are available. Another component has missing engineering documentation. A useful review makes both issues visible: purchasing needs to address the shortage, and engineering needs to supply or clarify the missing information. The team can see how these conditions relate to the same planned build, while keeping inventory coverage and engineering readiness distinct. Let AI explain the findings and support exploration The AI assistant can organize review findings for engineering, purchasing, or a build meeting. It can help people investigate which findings require engineering action, which components have shortages, and what changed after the latest correction. Its explanations should lead back to the affected Items, source information, and check results. For a question such as “How many units can we build with current inventory?”, AI should request the appropriate calculation and explain its result. Arithmetic and defined validation belong in software tools; the assistant makes their outputs easier to understand and investigate. As the available context and tools expand, scenario exploration can help teams compare options and identify which decisions or additional evidence they need. Preserve the review as Product Memory A useful review produces more than corrected data. It also produces decisions. Why was a warning accepted for a prototype? Which source established the Item identity? Who resolved the drawing question? What evidence showed that the condition was corrected? These decisions form part of the product’s history. OpenBOM’s collaborative review experience connects findings with comments and tasks. Our Product Memory direction is to preserve the evidence, reviewer decision, action, and recheck result together. Completing a task and resolving its underlying condition are separate events. Updated evidence and a recheck should establish closure. This is how Product Memory becomes practical: preserve what the team encountered, what it decided, and what changed. Future reviews can then use that context instead of reconstructing it from email and people’s recollections. Start with one product and one useful result The most useful first AI workflow has a manageable scope. Choose one product, one build quantity, and a small set of readiness conditions. Organize the source information, inspect the mappings, calculate demand, run the checks, and review the findings with the people responsible for the work. Measure whether the process finds known problems, produces relevant observations, makes evidence easy to inspect, and reduces preparation and clarification time. Then correct the inputs and repeat the review. That is the direction I see for practical AI in PLM: smarter ways to import, analyze, decide, and interact with product information, together with focused workflows that help teams get a job done. PDF import and AI BOM Health provide current examples. Universal ingestion and our exploration of build readiness show how we intend to extend the same foundation. Best, Oleg

group.mercedes-benz.com · Oct 20, 2026

PLM Software · AI in Product Development

Paradigm shift in product development: With PLM.AI (Product Lifecycle Management), Mercedes-Benz is creating an AI-native digital backbone. It establishes an end-to-end, intelligent knowledge base that connects development, procurement, production, sales and after-sales. As a result, new products and technologies can be brought to series maturity faster, more efficiently and more sustainably in the future. This can reduce development time by up to 30 percent, with efficiency gains of up to 50 percent expected in individual engineering processes. Reduced development time does not come from making compromises. It comes from helping teams make better decisions earlier by connecting the right people with the right information at the right time. This reduces complexity, accelerates innovation and enables new technologies to reach customers sooner. At the same time, the standards that define every Mercedes-Benz remain unchanged. The commitment to quality and safety is as high as ever. A Mercedes Benz developed with PLM.AI will still be unmistakably a Mercedes-Benz – defined by the same craftsmanship, engineering excellence and desirability, simply brought to customers faster. With PLM.AI, we are creating the digital backbone for the future of product development at Mercedes-Benz and bringing new technologies to series maturity up to 30 percent faster. This will make us faster, more connected and more capable of learning. At the same time, we are creating a foundation that connects vehicle development, production and customer experience in a new dimension. Our high standards for quality and safety remain unchanged. > Jörg Burzer > Member of the Board of Management of Mercedes-Benz Group AG. Chief Technology Officer, Development & Procurement 100-percent AI-ready: PLM data is made usable for AI applications Artificial intelligence is already firmly embedded in product development at Mercedes-Benz. AI agents support a wide range of tasks, from optimising material costs or certification and homologation processes to detecting and resolving faults or supporting software development. While these AI agents are currently used primarily within individual process steps, PLM.AI takes AI to the next level: Mercedes-Benz is connecting different applications across development processes, enabled by standardised data models and the end-to-end integration of development data. This creates a seamless digital thread built on consistent data and clear standards. Information becomes available in real time, coordination is simplified and innovations can be implemented faster. With an investment of around half a billion euros, Mercedes-Benz is paving the way for a new generation of product development that is more connected, data-driven and intelligent than ever before. This allows a more holistic assessment of complex interdependencies and faster translation of new technologies into customer benefits. PLM.AI helps employees analyse complex relationships across functions, intelligently connect relevant information and identify trade-offs at an early stage. AI-powered simulations and development processes enable more targeted use of physical testing, which could be reduced by half over time. This allows more efficient use of resources and earlier validation of development decisions. Meanwhile, the focus remains on people, and the responsibility for product decisions remains with the teams. One example is weight optimisation. Changes to individual components affect aspects such as costs, crash behaviour, hardware and software functions as well as production processes. Specialised AI agents will be able to analyse these interdependencies simultaneously. Development teams thus receive an overall picture faster, enabling them to evaluate solutions that account for safety, quality and commercial considerations. New potential is also emerging in after-sales through the linking of product, service and operating data. Two strong technology partners for PLM.AI Mercedes-Benz is collaborating with two strategic technology partners for PLM.AI, namely CONTACT Software and Siemens. Together, they are contributing significantly to modernising product development and implementing the PLM.AI architecture. Open all CONTACT Software Close CONTACT Software provides the open PLM core layer with CONTACT Elements. This gives Mercedes Benz the modular and scalable foundation needed to integrate product data, processes and applications along the product development process and to flexibly incorporate future AI technologies. Siemens Close Siemens supports Mercedes-Benz with comprehensive solutions and know-how in the areas of AI-enabled automotive design, simulation and digital validation for the digital twin. This enables key engineering domains to be interconnected and accelerated through the shared data and process foundation of PLM.AI. Mercedes-Benz leads the end-to-end transformation and defines the target vision, target architecture, data models and governance of PLM.AI. The company also manages global implementation within an agile transformation programme and continuously develops the new digital backbone. PLM.AI will form the cornerstone of Mercedes-Benz’s engineering ecosystem – as the basis for fully integrated, data-driven and AI-supported development across all vehicle programmes. At the same time, Mercedes-Benz retains full ownership and control of its data as well as the AI models developed and used on its basis. ,xPosition=1.0,yPosition=0) Artificial intelligence at Mercedes-Benz. At Mercedes-Benz, we see artificial intelligence as a key technology and a driver of innovation. We apply AI across the entire value chain. Artificial intelligence overview