PLM news, announcements and industry trends.

propelsoftware.com · Oct 27, 2026

AI in Product Development · Industry Trends

Redwood City, Calif ., October 20, 2026 - Propel Software , creator of the first product value management (PVM) platform that transforms how businesses create, market, sell, and service products, provides its annual business and technology predictions for the coming year. Propel CEO Ross Meyercord identifies five key trends for 2027. AI Shifts From SaaS Killer to SaaS Kingmaker The SaaSpocalypse narrative will give way to a new reality: AI and SaaS grow stronger together. AI companies will find more value in partnering with established SaaS providers, while SaaS companies will use AI to create sharper, more valuable customer experiences. The gap will widen between platforms with deep, connected capabilities and those with cursory bolt-on features. SaaS providers that move quickly to connect their data, workflows, and AI strategy will be positioned for sustained growth. Those with disconnected data and processes will lose out on the next round of AI-driven growth. AI Evolves from Human in the Loop to Human in the Audit Trail As experienced subject matter experts train AI on proven processes, their knowledge will become embedded in the systems teams use every day. Over time, newer employees may not have the same depth of experience to challenge AI recommendations. For established, repeatable work, organizations will increasingly trust AI to make the right call and complete tasks faster without constant human review. These tasks will be flagged as AI generated with detailed audit logs to allow for periodic reviews and audits by humans. Human expertise will remain essential for designing new processes, handling exceptions, and setting the standards AI follows. Care Continues its Move From Facilities to the Home Healthcare delivery will continue shifting beyond hospitals and ambulatory surgery centers and into patients’ homes. An aging population, combined with advances in wearables, remote monitoring, connected implants, and home diagnostics, will make this model more practical, necessary and cost effective. MedTech companies will need to design products and product experiences for continuous use outside clinical settings. That requires reliable product data, connected teams, and a clear view of every change across the product lifecycle. AI-assisted monitoring of this data is a must as the volume of inbound data will overwhelm human teams. Manufacturers Rebuild Their Tech Stacks to Optimize AI Companies will shift from adding AI that supports their systems to adding systems that support their AI. That means cleaning data, connecting sources, and investing in solutions AI needs to do useful work. AI and Model Context Protocol (MCP) connections will help bridge some systems, but they won’t replace missing data or capabilities. Expect manufacturers to invest in system of record solutions, such as product lifecycle management (PLM) and application lifecycle management (ALM), that give AI access to bills of materials, connect quality records to product changes, or link service history to engineering data. Investment will focus on giving AI reliable context, not simply adding another tool. AI Moves From Boardroom Sizzle to Everyday Results AI priorities will shift from high-visibility demonstrations to practical applications that improve daily work. Manufacturers will focus less on occasional, headline-grabbing challenges and more on repetitive, cross-system tasks that consume time and slow operations. The next round of agents will automate routine engineering handoffs, verification tasks, and delivery workflows, with success measured by shorter cycle times, less rework, smoother delivery, and lower cost per change. Executive teams will judge AI by its impact on throughput, quality, risk, and margin, not the attention a pilot attracts. To arrange an interview with Ross Meyercord to discuss any of these trends further, please contact laurap@chapmancommunicationsgroup.com . About Propel Software Propel helps product companies grow revenue and increase business value. Our product value management platform connects commercial and product teams to optimize decision making, drive process efficiencies, and engage customers with compelling products and experiences. Propel has a proven track record of improving product quality, speeding time to revenue and profit, and improving customer satisfaction. Recognized as one of America’s fastest growing private companies on the Inc 5000, Propel is also a 5x Deloitte Technology Fast 500 winner, and one of Fortune America’s Most Innovative Companies. Built on Salesforce, Propel drives product success for hyper growth startups, corporate pioneers, and Fortune 500 leaders in the high tech, medtech and consumer goods industries. For more information, visit propelsoftware.com and follow us on LinkedIn. Unlock the full value of your product data. Experience faster growth with a truly unified platform, connecting product, quality, and go-to-market teams. Get Started 835 Main Street Redwood City, CA 94063 info@propelsoftware.com (408) 755-3780 Products PVM Platform Propel PLM Propel PIM Propel QMS Propel One AI Design Hub Resources Resource Library Blog Podcast Glossary Support Help Center Trust Center Status Company About Us Careers

newsroom.accenture.com · Oct 27, 2026

Digital Thread & Digital Twin · CAD, CAE & Simulation

BERLIN; Oct. 21, 2026 – TÜV SÜD , a globally recognized testing, inspection and certification organization headquartered in Germany, and Accenture (NYSE: ACN) are collaborating to accelerate the independent certification process for industrial automation with NVIDIA technologies. At NVIDIA GTC Berlin 2026, the companies are presenting a joint concept of a “Physical AI Safety Suite” to help companies safely deploy physical AI equipment such as Autonomous Mobile Robots (AMR) alongside human workers faster. Using digital twins, AI agents and simulations, the Physical AI Safety Suite is designed to speed up the automation of factories and warehouses required to increase manufacturing productivity. url https://play.vidyard.com/wRzW2ozwCfLVAC9neHPekk caption NVIDIA GT Berlin 2026: TÜV SÜD and Accenture use NVIDIA technologies to help deploy robots and other physical AI equipment safely alongside human workers faster. Established industry standards provide the basis for ensuring that automated facilities are designed and operated safely. An example is ISO 3691-4, which specifies safety requirements for driverless industrial trucks and systems, including AMRs. In many industrial countries, certification against such norms is mandatory before companies can commission automated facilities and deploy new physical AI equipment. Even in markets where certification is voluntary, following industry-specific standards issued by recognized standards bodies is considered best practice. But navigating the intricate web of standards can slow down the adoption of physical AI-enabled automation solutions. At a medium-sized production plant, the iterative coordination, validation and acceptance of safety concepts can take several weeks. From manual to simulation testing Presented at NVIDIA GTC Berlin , the Physical AI Safety Suite partially digitizes the manual assessment of facilities, equipment and preparation of certification packages, using digital twins and AI-powered simulations. TÜV SÜD estimates that the digital process could bring production online up to 30% sooner. Not only does it reduce physical dependencies such as installed equipment and site visits; companies can also address safety and certification issues earlier to avoid costly reworks in operation. image width 800px caption Using digital twins, AI agents and simulations, TÜV SÜD and Accenture’s Physical AI Safety Suite is designed to speed up the automation of factories and warehouses. “Physical AI enables robotic systems to become increasingly adaptive while also paving the way for an independent, evidence-based digital certification process,” said Patrick Vollmer, CEO and Chairman of the Board at TÜV SÜD AG. “Through our Physical AI Safety Suite, which leverages simulation-generated evidence, we can support compliance-readiness much earlier in the engineering lifecycle without compromising the rigor safety standards require. Assessing automation concepts before the first robot is deployed reduces uncertainty in live operations and helps drive higher levels of safety, efficiency and performance across production and logistics.” “Too often, companies test automation concepts against industry standards after the equipment, layouts and workflows have already been established,” said Sarat Maitin, Supply Chain and Engineering Lead for Accenture in Central and Eastern Europe. “The Physical AI Safety Suite brings these steps forward to when companies can still avoid safety incidents and costly rework. It also enables continuous monitoring of automated facilities. Both support faster deployment of physical AI-enabled automation needed to drive manufacturing productivity, particularly in Europe.” Connected checks on three layers The Physical AI Safety Suite concept assesses automation on three connected layers, with TÜV SÜD providing independent assessment and assurance, and retaining responsibility for certification: Layer 1: Simulating industrial layouts – Where can companies deploy physical AI safely? Using NVIDIA Omniverse libraries, the open Cosmos world model platform and the NVIDIA Isaac Sim open robotics framework, the Physical AI Safety Suite virtually assess routes, clearances, mixed-use zones and collaboration areas before robots are deployed or layouts are reconfigured. Then it generates a structured evidence package for TÜV SÜD review, covering operating-zone requirements, hazard and risk reduction standards, robot integration norms and control-system safety standards. Layer 2: Adding robotic fleets – How does Physical AI operate safely? Using NVIDIA Omniverse, Cosmos and Isaac Sim, the Physical AI Safety Suite simulates robotic fleets and their behaviors to identify operational constraints and potential hazards, such as workers becoming trapped by AMRs, before deployment. During operation, the layer uses the NVIDIA Halos safety platform and the NVIDIA IGX Thor edge AI platform to identify potentially hazardous situations beyond an individual robot’s view. AI agents built with the NVIDIA Halos Outside-In Safety Blueprint extend a robot’s perception to reason and act autonomously with facility-mounted cameras. This allows fleet management systems to respond earlier to improve safety and operational efficiency. Layer 3: Individual robots – What is Physical AI allowed to do? Even equipment complying with all applicable product regulations must be tested under its intended site-specific conditions, such as load, speed, floor conditions and lighting. Using Isaac Sim and Cosmos, the Physical AI Safety Suite creates and tests a long tail of certification-relevant scenarios that real-world testing could never practically cover. This, for example, allows engineering teams to find the best placement for safety sensors well before a physical prototype exists. image width 800px caption The Physical AI Safety Suite creates and tests a long tail of safety scenarios robots that real-world testing could never practically cover. Physical AI describes a mix of technologies, including digital twins, simulations, sensors, robots and AI models, which enables robots and machines to sense, decide, act and improve constantly under changing conditions and operate more autonomously in real environments. Accenture recently established a Center of Excellence that incubates, builds, scales and deploys physical AI solutions for warehouses and production facilities. * Accenture, NVIDIA and TÜV SÜD are demonstrating the Physical AI Safety Suite at NVIDIA GTC Berlin at the Accenture booth (#3056) and in a joint speaking session by Patrick Vollmer (TÜV SÜD) and Sarat Maitin (Accenture) on the main stage (Exhibit Hall Theater) on October 22, 9:20 AM CEST. Journalists interested in scheduling a demo presentation and/or briefing can contact [email protected] , +49 152 5250 7486 About TÜV SÜD Founded in 1866 as a steam boiler inspection association, the TÜV SÜD Group has evolved into a global enterprise. More than 30,000 employees work at over 1,000 locations in about 50 countries to continually improve technology, systems and expertise. They contribute significantly to making technical innovations such as Industry 4.0, autonomous driving and renewable energy safe and reliable. About Accenture Accenture is a business reinvention and technology company. Our ambition is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client-focused, AI-enabled, great place to work in the world. Our approximately 814,000 people bring deep insights across industries and functions, with capabilities spanning strategy, process, operating model, change management, talent, data, technology and AI. We deliver this expertise through consulting, managed services, proprietary assets and platforms, and increasingly, software. We bring innovation and new solutions informed by our experience across industries, enabling us to apply relevant insights, capabilities and leading practices from adjacent sectors to address emerging business challenges and opportunities. We serve more than 9,000 clients and generated revenues of approximately $74 billion in fiscal year 2026. Visit us at accenture.com . # Contacts: Alem Tedeneke Accenture +1 646 204 9191 [email protected] Dr. Thomas Wittek Accenture +49 152 5250 7486 [email protected]

Cadence · Oct 27, 2026

AI in Product Development · CAD, CAE & Simulation

Cadence Introduces Maestro, a Domain-Specialized AI Model Portfolio to Revolutionize Agentic Chip and System Design 26 Oct 2026 HIGHLIGHTS • Cadence Maestro portfolio introduces domain-specialized AI models purpose-built to power agentic chip and system design. • Vertically integrated reinforcement learning environment connects Cadence Maestro models, Cadence AI Super Agents and proven Cadence EDA and SDA tools to continuously improve engineering outcomes and drive faster time to market. • Initial design-workflow results show up to 30% higher quality and up to 5X greater token efficiency versus general-purpose foundation models. • Secure architecture enables the optimal combination of domain-specialized Maestro models, leading frontier and open-weight models with cloud and customer-controlled on-premises deployment. SAN JOSE, Calif.— Cadence (Nasdaq: CDNS) today announced the Maestro™ AI Model Portfolio, which powers Cadence® AI Super Agents across semiconductor and system design workflows. By integrating these models with reinforcement learning (RL) loops and its proven electronic design automation (EDA) and system design automation (SDA) tools, Cadence delivers a deeply integrated agentic engineering platform designed to improve results, accelerate time to market and continuously enhance performance through recursive self-improvement. The Maestro technology offers customers a secure, Cadence-managed platform that combines exceptional domain-specialized intelligence and expertise with the control, choice, and flexibility they need to innovate with confidence. The Maestro portfolio spans multiple model sizes and architectures, combining domain-specialized post-trained large language models (LLMs), trained decision models and self-improving agent skills. Integrated with Cadence AI Super Agents—including AuraStack™, ChipStack™, InnoStack™, and ViraStack™ AI Super Agents—as well as its proven EDA and SDA tools and reinforcement learning, the Maestro portfolio brings specialized intelligence to complex engineering workflows through an optimal blend of frontier, open-weight and domain-specialized models across cloud and on-premises environments. “Revolutionizing complex engineering tasks requires deep domain-specialized intelligence,” said Anirudh Devgan, president and CEO at Cadence. “Combining our Maestro portfolio of domain-specialized models with leading frontier and open-weight models enables our AI Super Agents to drive Cadence EDA and SDA tools with exceptional skill and efficiency to transform chip and system design.” “Agentic chip design requires AI that can generate design content, evaluate tradeoffs and learn from verified engineering results,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “Our collaboration with Cadence brings NVIDIA Nemotron, OpenShell and deep engineering expertise to Maestro’s domain-specialized models. By grounding reinforcement learning in feedback from EDA tools and deterministic solvers, we can teach agents to make better design decisions based on how well their work meets engineering objectives and constraints.” “We are deploying Cadence’s InnoStack AI Super Agent into our production design environment,” said Hisato Yoshida, COO of Socionext. “By combining agentic AI with advanced EDA tools for physical design, we expect up to 5X overall productivity gains while maintaining design quality. We see the Maestro portfolio as the next step in driving even greater efficiency across our design process.” “Ultra-low power devices require innovation across disciplines. Agentic AI unlocks new power opportunities,” said Garima Sharda, vice president of engineering at Silicon Labs. “By leveraging the Cadence Verisium Debug as part of ChipStack AI Super Agent, our engineering teams have achieved acceleration in root-cause analysis and end-to-end debug during pre-silicon low power verification. We look forward to realizing further benefits through Cadence’s Maestro domain-specialized models.” A Self-Improving Learning Environment for Engineering AI Initial results show up to 30% improvement in quality of results and up to 5X greater token efficiency versus a general-purpose foundation model. These gains come from a vertically integrated learning environment combining Cadence domain knowledge, curated engineering datasets, AI Super Agents, EDA and SDA tools, and evaluation harnesses. Cadence’s principled algorithms and deterministic solvers evaluate outputs against engineering objectives, constraints and ground truth, creating closed and verified feedback loops for reinforcement learning and recursive improvement. Domain Intelligence with Intelligent Model Choice The Cadence learning environment unifies complementary AI capabilities in a single workflow. Frontier and open-weight models provide broad language, reasoning and multimodal capabilities; the Maestro AI Model Portfolio provides domain expertise for complex chip and system design. Given a high-level objective, Cadence AI Super Agents interpret design intent, decompose tasks, orchestrate the Maestro portfolio with other leading frontier and open-weight models, and drive Cadence EDA and SDA tools. The combined solution interprets specifications, generates design content, analyzes results, evaluates tradeoffs and guides decisions based on engineering requirements, cost and security. The Maestro portfolio, including NVIDIA Nemotron and other models, can be further post-trained on a customer’s proprietary data, designs, and workflows and, by balancing EDA and SDA tool calls with LLM invocations, it significantly reduces token usage while maintaining execution efficiency. The complete Cadence agentic environment—including AI Super Agents, Maestro AI Model Portfolio and EDA tools—can run on Cadence’s secure cloud platform, secure public clouds, or in a customer-controlled on-premises environment to meet data privacy, security and deployment requirements. Cadence and NVIDIA are also working together on agent security through integration with the NVIDIA OpenShell Reference Architecture. Availability The first Maestro models are available to select early access customers in the fourth quarter of 2026. About Cadence Cadence is a market leader in AI and digital twins, pioneering the application of computational software to accelerate innovation in the engineering design of silicon to systems. Our design solutions, based on Cadence’s Intelligent System Design™ strategy, are essential for the world’s leading semiconductor and systems companies to build their next-generation products from chips to full electromechanical systems that serve a wide range of markets, including hyperscale computing, mobile communications, automotive, aerospace, industrial, life sciences and robotics. In 2025, Cadence was recognized by Fortune as one of the world’s top 100 best companies to work for. Cadence solutions offer limitless opportunities—learn more at www.cadence.com. Category: Featured Steve Gartner sgartner@cadence.com Source: Cadence Design Systems, Inc.

Autodesk · Oct 27, 2026

CAD, CAE & Simulation

When launching AutoCAD product, after a successful installation, it fails to start. In some scenarios, an Error in Event Viewer or an Error with the acad.exe running in Task Manager appear. Environment: Windows 11 Causes: • Acad.exe is stuck running in Processes or Background processes. • Corrupted Microsoft Visual Redistributable. • Windows laptop settings issue. • Security software conflict. Solution: Acad.exe is stuck running in Processes or Background processes: 1. Close all Autodesk products. 2. Restart the computer. 3. Start the product. Corrupted Microsoft Visual Redistributable: • In Event Viewer > Windows Logs > Application there may be an Error callout for acad.exe relating to a MSVCP140.dll file. • In Control Panel the Microsoft Redistributables (x86 and x64) year version pairs, are missing, do not match the build number, or are missing the file size information. 1. Reinstall these components, refer to: How to remove and reinstall Microsoft Visual C++ Runtime Libraries. 2. Restart the computer. 3. Start the product. Windows laptop settings issue: On a Windows laptop, adjust settings as per: Autodesk desktop software runs slow, crashes, fails to start or install on a Windows laptop. Security software conflict: Adjust security software, refer to: How to set up antivirus (AV) exclusions to improve stability and performance of Autodesk software User account permission issue: What user permissions does AutoCAD require? See Also: • AutoCAD fails to start with Program Error Report and crashes. • AutoCAD Products freeze, hang, or crash during startup. Products: AutoCAD Products; Was this information helpful? YesNo Need help? Ask the Autodesk Assistant! The Assistant can help you find answers or contact an agent. Ask the Assistant What level of support do you have? Different subscription plans provide distinct categories of support. Find out the level of support for your plan. View levels of support

CIMdata · Oct 27, 2026

AI in Product Development · PLM Software

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• Building a Consensus Agreement on an A&D MBD Minimum Position • Getting People to Care: The Case for Organizational Change to Support Digital Transformations • The State of the PLM Economy • The Next Step: Being Prepared for the Next Disruption • Why an mBOM Alone Isn’t Sufficient • Aerospace OEM to Supplier Collaboration: A Look Inside their Future Vision • Complex Products Are Safer with PLM and Digital Twins • The Digital Thread is Really a Web, with the Engineering Bill of Materials at Its Center • Education: Driving Systems Modeling and Simulation Understanding & Adoption • PLM Solution Evaluation & Selection • “Should I Stay, or Should I Go?”: Cost, Risks, and Rewards of Moving-On from a Legacy PLM Solution • Structured Content: A Critical Component of a Robust PLM Strategy • 2021 PLM Market Update • 2021 PLM Status & Trends • Data Governance: Avoiding Detours on your Digital Transformation Roadmap • Defining a Practical PLM Roadmap • Education: Driving Systems Modeling and Simulation Understanding & Adoption • Research on Cloud/SaaS Adoption in PLM • CAD Trends—Real Change or Marketing Hype? • The “M” in MBx • Cloud and SaaS in the PLM Economy • M&A: Using PLM to Achieve Promised Synergy • The State of the PLM Economy • Today’s PLM Trends & Tomorrow’s Disruptors • Transforming Your Enterprise Application Architecture • CAD Maturity: Identifying Your Strategic and Tactical Ranking • The Aerospace & Defense PLM Action Group as a Model for Industry Collaboration • Bridging Information Silos to Improve Product Decisions • Putting the SAP-Siemens Partnership in Context • Why is PLM so Hard? How to make the complex simple. • Data Governance in the Era of Digital Transformation • Managing Disruption: Using PLM to Support Working from Home • Systems Modeling and Simulation: An Introduction • Digital Skills Transformation: Assessing, Defining & Enabling the Workforce of the Future • 2020 PLM Market Update • Generating Actionable Insight While Predicting Manufacturing Operation Faults - A CIMdata Research Update • PLM Benefits, Metrics, and ROI • 2020 PLM Status & Trends • Potential Impacts of COVID-19 on the Global PLM Market • Managing the Lifecycles of Large Capital Projects and their Assets • Extended Product Data – Making a Product Salable • Making Multiple-View Bill of Materials Management a Reality • PLM in Medical Devices • PLM and ERP Drive Digitalization • Digital Engineering at Cummins • Today’s Disruptions: Sub-optimization Is Not an Option • Anyone Measuring Digital Transformation? • Lifecycle Digitalization • A Call for Collaboration Revolution • The State of the PLM Economy • The Rise of Simulation – Redux • Beyond Generative Design • PLM Status & Trends • PLM at the Core of Analytics-Driven Innovation • Digitalizing Reality • Augmented Intelligence is Critical for Robust Digital Transformation • The As-Maintained BOM: Managing the Product Lifecycle After the Sale • Innovators Need PLM: Why PLM Should be a Bigger Priority than ERP • Securing the Additive Manufacturing Digital Thread • Setting up a PLM Program Performance Dashboard that Performs • Driving a PLM Center of Excellence • PLM Trends and Global Market Risks to Medical Device Companies • Barriers & Solutions to Industrializing Additive Manufacturing • The State of the PLM Economy • PLM’s Application in the CPG Industry: Protecting the Brand • Blockchain in PLM for Digital Asset Innovation • Blockchain for Enhancing the Value of PLM • Model-Based Enterprise & Standards • Topology Optimization Changing the Face of CAD • Digitalization and PLM - The Next Big Thing or All Hype? • Blockchain Technology and PLM Usage • Industrializing Additive Manufacturing to make practical end-use products • What does Semantic Technology do for Robust Design? • Multiple Views into the Bill of Information • Creating Value When PLM and ERP Work Together • Design for Additive Manufacturing—reinventing how we design everything we make • Generative Design: The Changing Nature of Product Development • Managing PLM Solution and Data Obsolescence • Semantic Web Technology helps Design-for-Reliability • Rip & Replace or Embrace & Extend to enable a PLM Platform • The Essential Role of PLM in the Adoption of Additive Manufacturing (AM) into Mainstream Production • Simulation-Driven Systems Development: Challenges and Opportunities • How to apply IoT, Industry 4.0 & Predictive Analytics in the Manufacturing Domain • The State of PLM on the Cloud • Digitize Your Enterprise Network Management for Competitive Advantage • How to Claim the Benefits of PLM for your Business • Why Connected Intelligent Products Need Semantic Web Technology • Navigating the PLM Standards Universe • Connecting the Clouds in AEC & EPC—Where to Start? • How do you ensure your connected intelligent products can be trusted? Deploy knowledge systems for reliability design • Transforming AEC – Obstacles and Opportunities • Improving Collaboration in the Aerospace Design Chain • Failure Knowledge Capture and Reuse for Designing Dependable Software-Intensive Products • Using Agile Methods to Speed Time to Value in a PLM Deployment • Ensuring the Dependability of Smart Connected Products • Materials Engineering & PLM for the Automotive Industry • Developing a Sustainable PLM Strategy • Technology Impact on Ideation and Idea Management • PLM Support for Knowledge Capture & Management • IoT – Hype or Value? • Simulation Governance: Managing Simulation as a Strategic Capability • Training Best Practices for Solution Providers Education: The key to unlock PLM market value • Enterprise Innovation Management Solutions - Connecting the Dots! • Project Management Practices for PLM Success • Managing Product Changes • Innovation in the Digital Age • The Internet of Things - What does it mean for PLM? • PLM for Process-Based Products • High Tech, PLM, and the Cloud • PLM & ERP: What’s the Difference, and Why Should you Care? • Not Getting The Most out of PLM? How to Close the Value Gap • PLM’s Expanding Footprint: Enabling Innovation • PLM Solution Evaluation & Selection • PLM-Enabled Digital Transformation Appraisal Workshop • Consulting • Experience & Expertise • Consulting Approach • Consulting for Industrial Organizations • Transformation • PLM Checkup • MBE Strategy Development • Consulting for Solution Providers • Focused Consulting Practices • Research • Industry Action Groups • CIMdata Community • Industry Research • PLM Market Analysis Report Series • Custom Research • Memberships • CIMdata Community - Platinum • CIMdata Community - Gold • CIMdata Community - Silver • CIMdata Community - Financial Analyst • Aerospace & Defense PLM Action Group • High Technology Electronics PLM Action Group • Medical Device PLM Action Group • CIMdata Community - Industry Consortium • Events • PLM Road Map™ Conferences • Industry Events • PLM Certificate Program • Market & Industry Forums • Webinars • CIMdata Supported Events • CIMdata Community Events • Pre-2014 Archived News & Press Releases Home > News > CIMdata Publishes Siemens-Sponsored eBook on the State of AI in PLM Tuesday, October 27, 2026 CIMdata Publishes Siemens-Sponsored eBook on the State of AI in PLM ANN ARBOR, Michigan, USA, 27 October 2026—CIMdata, Inc., the leading global PLM strategic management consulting and research firm, announces the release of an eBook titled “ What’s the State of AI in PLM? ” based on CIMdata research sponsored by Siemens Digital Industries Software and other PLM solution providers. The publication reports on the current state of AI adoption in PLM across industrial companies, software providers, and service providers, identifies barriers to deployment, and pairs each finding with practical guidance on how to address them. AI is now a central topic in product development, yet the research shows that deployment remains in its early stages. Most industrial companies are in the early stages of their AI journey, and they apply AI to fewer than a quarter of their projects. Investment plans run well ahead of deployment: at least 80% of each group surveyed expects AI investment to grow over the next two years. The obstacles the study identifies are organizational, not technical. Data quality, integration with existing systems, confidence in validating AI outputs, and governance are the factors that most often determine whether AI investment reaches production. “The gap between interest in AI and results from it is not primarily a technology problem,” noted Peter Bilello, CIMdata’s President and CEO. “What holds adoption back is data quality, integration with the systems engineers already use, and the ability to validate what AI produces within engineering processes. Organizations that address those issues deliberately, rather than waiting for the next generation of AI models, are the ones converting investment plans into working deployments.” Findings reported in the eBook include: • Adoption is real but early: Most industrial companies remain in the earliest stages of their AI journey, with AI applied to fewer than a quarter of their projects. • Data quality is the main obstacle: 60% of industrial companies name data quality a top-three adoption challenge, and service providers independently report the same figure for their clients. • Integration is the top selection criterion: Integration with existing systems is the leading factor in choosing an AI solution, cited by 62%, ahead of proven ROI, security, and production-readiness. • Validation confidence is low: Only 5% of industrial companies are very confident in their ability to validate AI outputs, indicating a gap between pilot and production. • Available AI capability is underused: Only 8 to 33% of industrial companies use the AI already built into their primary tools. Siemens Digital Industries Software sponsored the eBook and is one of the sponsors of the CIMdata AI in PLM research program on which the eBook is based.

Beyond PLM · Oct 25, 2026

PLM Software · AI in Product Development

AI-generated summary: Trustworthy product data requires more than accurate records, approved revisions or connected systems, argues Beyond PLM author Oleg. Information must be suitable for the specific decision being made, with clear applicability, supporting evidence and accountable decision-makers. Exploring the implications for AI-enabled PLM, he examines digital threads, governance and “product memory” as potentially complementary approaches, while leaving open what future architectures should guarantee. A production-readiness scenario illustrates the problem: engineering has an approved design, purchasing has confirmed orders, manufacturing has a production plan, and a supplier has emailed a proposed component substitution. Each record may be correct independently, yet the team cannot confidently proceed without knowing whether the substitution requires an engineering change, whether it has been accepted, and whether orders and plans describe the same configuration. Engineering, purchasing, manufacturing, service and suppliers maintain information for different purposes, so discrepancies can reflect legitimate differences or unfinished handoffs. Drawing on CIMdata’s work on data governance, the article emphasizes policies, responsibilities, decision rights and accountability across organizational boundaries. Connecting records can expose disagreement; resolving it still requires authority and communication. Public sources introduce additional verification challenges. Manufacturer specifications, distributor catalogs, material databases, standards, regulatory notices and discontinuation announcements may inform product decisions, but accessibility does not establish suitability. An AI-discovered claim that two components are interchangeable must be checked against the exact versions, operating conditions and manufacturer documentation. Citing the UK National Cyber Security Centre, Oleg distinguishes provenance—evidence of information’s history—from proof that its contents are true. Organizations must also consider how to preserve external evidence if a webpage changes or disappears. Finding a document should begin evaluation, not automatically make it accepted evidence. Trust is therefore contextual rather than a permanent designation. An approved drawing may support design review but not establish purchasing availability or the installed configuration of a serviced unit. Jos Voskuil and Rob Ferrone’s discussion of data quality reinforces this point: estimated costs can be sufficient during design but inadequate when ordering, while distrust can drive offline reporting and conflicting versions. Oleg proposes five checks: source, applicability to a product/configuration/time period, validation and remaining assumptions, changes since acceptance, and responsibility for resolving uncertainty. The digital thread provides traceability across lifecycle stages and systems, linking requirements to designs, changes to implementation, and delivered units to configuration records. Michael Finocchiaro’s thread-centric PLM perspective connects this architecture with trustworthy engineering AI. Nevertheless, links alone do not establish agreement: connected records may concern different configurations, a visible change may remain unimplemented, and an attached document may not have been accepted. AI must similarly distinguish approved decisions from suggestions, identify revision mismatches and missing evidence, and separate established records from interpretation. The assurance needed should rise with the consequences of summarizing an issue, recommending a substitution or executing an order. AI could help build and maintain these connections by comparing information and flagging misalignments. The article references a pilot described by Ferrone combining expert-defined quality rules, AI anomaly detection and initial human review. Proposed relationships, however, must remain distinguishable from verified ones, be open to inspection and challenge, and potentially be reassessed when sources change. Other possibilities include assembling evidence around individual decisions, sharing context while organizations retain their own systems, and continuously checking whether earlier decisions remain valid. These are exploratory directions, not established solutions. “Product memory” could preserve decisions, assumptions, disagreements and reasoning—for example, why a substitution was approved only under particular operating conditions. It might help people and AI avoid overgeneralizing approvals or revisit them when evidence changes. But explanations can be incomplete, assumptions outdated and informal suggestions never accepted. Voskuil’s challenge about how knowledge graphs “unlearn” underscores the need to correct interpretations rather than merely retain history. Oleg concludes that traceability is foundational but insufficient without context, timing, agreement and authority. He keeps the architectural question open and discloses that his role as OpenBOM co-founder and CEO, including work on connected product data and Product Memory, shapes his perspective.

Beyond PLM · Oct 24, 2026

PLM Software · AI in Product Development

AI-generated summary: Connecting AI to product lifecycle management can make engineering information easier to find and workflows easier to execute, but it also exposes security assumptions embedded in legacy software, customizations and integrations. Beyond PLM author Oleg argues that AI adoption should begin with understanding what an assistant can access, who maintains each connected component and how access can be withdrawn. The concern extends to companies that have not deployed AI: attackers can use it to investigate existing systems more quickly. Drawing on Nate B. Jones’s security analysis, the article calls for evidence-based assessment rather than assuming that either cloud hosting or newer software guarantees security. Several incidents illustrate the dependency risks. PHP issued emergency fixes for vulnerabilities that, in some configurations, could permit an attack before application code executes. Researcher Scott Helme documented eight weak eMudhra intermediate certificate keys, including one with a value of four. AI integration software provider LiteLLM disclosed a compromised release. These examples do not establish vulnerabilities in any particular PLM product, the author stresses, but demonstrate why underlying components and chains of access deserve scrutiny. Cloud PLM security depends on architecture and operational responsibilities. A customized installation hosted on cloud infrastructure may retain its own versions, database and upgrade schedule, whereas centrally operated SaaS can simplify maintenance while making tenant isolation and shared-update validation especially important. AWS’s shared responsibility model illustrates that infrastructure hosting does not eliminate application-level obligations. Legacy customization adds continuing maintenance demands: in a hypothetical implementation, a custom release workflow and old ERP connector delay an urgent patch while teams validate compatibility and replace a dependency. The author frames this as a trade-off between disruption and exposure, citing NIST’s treatment of patching as preventive maintenance. Jones argues that faster AI-assisted investigation by attackers could make lengthy upgrade cycles more consequential. AI also changes the practical significance of permissions. An engineer who already has overly broad access across customer projects might previously have struggled to locate and combine documents; an assistant could assemble them in one conversation. Shared integration accounts with privileges exceeding the employee’s authorization compound the risk, which OWASP identifies as excessive agency. Authorization must therefore be enforced by connected systems, not merely requested through model instructions. Data governance must also follow drawings and documents into extraction services, search indexes, conversations and logs. Revoked supplier access must propagate downstream, and retained content must preserve revision context so that an otherwise authorized answer does not rely on obsolete engineering information. Integration tools become security-critical systems themselves. LiteLLM’s updated investigation traced its attack to a master key exposed months earlier and never revoked. An internal agent platform held an organization-owner GitHub token, enabling access to further credentials and interference with releases. PLM adapters, AI gateways, automation services and MCP servers warrant similar examination of credentials, reach and maintenance ownership. Stolen AI provider keys can also generate computing charges. Separately, external documents introduce indirect prompt-injection risks: a hypothetical supplier PDF might try to persuade an agent to retrieve unrelated drawings and transmit them elsewhere. Success would depend on available tools and controls, but external text must never substitute for authorization; capabilities and approval requirements should be constrained outside the model. Manufacturing consequences extend beyond intellectual-property theft. Unauthorized changes to quantities, approved suppliers or revision status can affect downstream operations, while an unavailable release process can delay purchasing or production. The article distinguishes assistants that explain changes from agents empowered to execute them. For consequential actions, the author advocates visible proposals, supporting evidence, required approvals and records identifying the initiating user and actual outcome. AI readiness should therefore be demonstrated through denied-access tests, permission revocation, misleading supplier documents, integration disconnection and incident reconstruction. Updating compromised software alone neither revokes stolen credentials nor establishes whether data was copied. AI can help defenders examine code, dependencies and configurations, but its findings require verification and its investigative access needs limits. The conclusion favors evaluating both older applications and new AI products on evidence and designing controls from the outset. The author discloses that his role as OpenBOM co-founder and CEO, and work on connected product data and Product Memory, shape his perspective.

Cadence · Oct 24, 2026

AI in Product Development · CAD, CAE & Simulation

Socionext Inc., a global leader in custom SoC design and development, is actively advancing its SoC design and development capabilities through agentic AI and expanding the application of AI-driven design to the physical design of large-scale system-on-chip (SoCs) using advanced process technologies. For the expansion into physical design, Socionext is collaborating with Cadence (Nasdaq:CDNS). Actively Advancing Agentic AI Initiatives in SoC Design and Development Socionext has been integrating agentic AI into front-end design to improve the efficiency of design and verification tasks, support engineering analysis and decision-making, and leverage accumulated design knowledge and expertise. Beyond improving the efficiency of individual tasks with AI, Socionext is also integrating AI agents into its design and development processes to further enhance engineering productivity and design quality. Through these initiatives, Socionext aims to create a development environment that enables engineers to focus on higher-value design decisions and innovation. Expanding AI-Driven Design into SoC Physical Design Socionext is expanding its AI-driven design approaches into physical design. As physical design becomes increasingly complex for large-scale SoCs using advanced process technologies, effectively integrating AI agents with advanced EDA tools and design flows is critical to addressing these challenges and further improving design efficiency. Through its collaboration with Cadence, Socionext is advancing these efforts and is applying AI-driven design to the physical design of a large-scale 3nm SoC. As part of these efforts, Socionext is applying the Cadence InnoStack AI Super Agent and plans to expand its use across SoC development projects. Integrating Engineering Knowledge into Physical Design Workflows Through retrieval-augmented generation (RAG), Model Context Protocol (MCP) support, customized AI skills, and AI agents, Socionext integrates internal design knowledge into practical physical design workflows. Current and planned applications include floorplanning, custom timing-analysis checks, and future checklist automation, helping make proprietary expertise more accessible and reusable across projects and teams. “As semiconductor design complexity grows, engineering organizations need better ways to capture and reuse expertise across projects and teams,” said Chin-Chi Teng, senior vice president and general manager of the Digital and Signoff Group at Cadence. “Socionext’s implementation demonstrates how the open and extensible InnoStack AI Super Agent enables customers to integrate their own methodologies and proprietary engineering knowledge into AI-assisted design environments. Together, Cadence’s technology and Socionext’s engineering expertise make valuable design knowledge more accessible and actionable across the organization.” “Socionext has already incorporated AI into front-end design activities and is applying AI-driven design to the physical design of a large-scale 3nm SoC,” said Hisato Yoshida, President and COO of Socionext. “Building on these efforts, we are advancing our AI-driven design capabilities through our collaboration with Cadence and applying the Cadence InnoStack AI Super Agent to SoC development projects. We expect to achieve up to 5X overall productivity gains through Cadence’s agentic AI workflow. By combining our accumulated design knowledge, methodologies, and engineering experience with Cadence technology, we aim to improve design quality and enable engineers to focus on higher-value innovation across the entire SoC development flow.” Expanding AI-driven Design Across SoC Development Beyond automation, Socionext aims to preserve and reuse valuable engineering know-how as a shared organizational asset across projects, teams, and future generations of engineers. This approach supports ongoing design transformation while maintaining the quality and reliability customers expect from Socionext. Looking ahead, Socionext plans to connect its front-end design AI initiatives with its expanding AI-driven physical design capabilities, creating a more integrated approach across the SoC development flow. In physical design, Socionext will continue its collaboration with Cadence and apply the resulting capabilities to additional SoC development projects. The company is also developing custom AI agents and engineering assistants that leverage internal expertise to strengthen productivity and knowledge reuse across the organization. About Socionext Inc. Socionext Inc., a leading global System-on-Chip (SoC) supplier, is a pioneer of the ‘Solution SoC’ business model. This innovative approach encompasses Socionext’s ‘Entire Design’ capabilities and offering of ‘Complete Service’. As a trusted silicon partner, Socionext fuels global innovation, providing superior features, performance, and quality that set its customers’ products and services apart in diverse domains ranging from automotive and data centers to networking, smart devices, and industrial equipment. Socionext Inc., based in Yokohama, operates offices across Japan, Asia, the United States, and Europe for development and sales. For more information, visit https://www.socionext.com/en. About Cadence Cadence is a market leader in AI and digital twins, pioneering the application of computational software to accelerate innovation in the engineering design of silicon to systems. Our design solutions, based on Cadence's Intelligent System Design™ strategy, are essential for the world's leading semiconductor and systems companies to build their next-generation products from chips to full electromechanical systems that serve a wide range of markets, including hyperscale computing, mobile communications, automotive, aerospace, industrial, life sciences and robotics. In 2025, Cadence was recognized by Fortune as one of the world's top 100 best companies to work for. Cadence solutions offer limitless opportunities.

Engineering.com · Oct 23, 2026

CAD, CAE & Simulation · Digital Thread & Digital Twin

AI-generated summary: Siemens Designcenter aims to connect product development through a digital thread linking design, simulation, manufacturing, electronics and lifecycle management. In this Siemens-sponsored article, company executives describe an integrated engineering environment intended to reduce manual data transfers, preserve design intent and support multidisciplinary collaboration. The offering combines modeling tools and industry-specific workflows with cloud deployment, AI development and immersive interaction with live CAD data. The problem addressed is the fragmentation of engineering information across disconnected applications. Siemens product management director Taylor Anderson explains that exporting and importing neutral formats such as STEP and IGES can introduce errors and lose design intent when applications lack associativity. Multiple files, formats and revision states can contribute to inaccurate bills of materials, rework and delays. Manual transfers also interrupt automated design iteration, whether driven by design-space exploration, optimization or AI. These difficulties increase when mechanical, electrical, analysis, manufacturing and inspection teams must coordinate their work. Designcenter is presented as a shared environment in which teams use the same real-time data without manual synchronization. Anderson claims Siemens uniquely combines mechanical CAD and electrical CAD capabilities with live bidirectional associativity and cross-probing between the two domains. The broader objective is to maintain data continuity so downstream teams receive accurate parameters directly from source models. Siemens associates this approach with faster time-to-market and more engineering time available for innovation and design quality, although the article provides no quantified performance results or named customer case studies. Modeling flexibility is a central element of the platform. Engineers can use parametric, direct, convergent, implicit and algorithmic modeling, along with synchronous technology. Solid, surface, faceted, subdivision and implicit geometry can coexist within a part file and even on different faces of the same body, reducing the need to switch tools. Deployment options include desktop applications, native browser-based applications and headless remote processes for Linux-based automation. Anderson also emphasizes support for coexistence with third-party tools, framing integration as compatible with an open engineering environment rather than requiring exclusive use of Siemens applications. The article describes integrated modules and industry- and process-specific capabilities available through a unified, value-based licensing token pool. This gives teams access to specialized tools on demand rather than requiring separate licenses for every application. For smaller organizations, Siemens identifies Designcenter Essentials and Designcenter Solid Edge as starting points, with a path to higher-end systems as requirements grow. A shared geometry kernel is intended to enable that progression without translating data. Anderson also says Siemens customers manage exceptionally large and complex mechanical assemblies while viewing complete products, rather than relying on workarounds or subsets; no assembly-size figures are supplied. AI, cloud and immersive technologies extend the connected-workflow strategy. Siemens says it is pursuing agentic AI approaches, while acknowledging that engineering AI must be reliable and avoid hallucinations. The article does not detail specific agentic functions or a delivery timetable. For cloud collaboration, Dan Staples, Siemens’ vice president of mainstream research and development, highlights a single file format across desktop and cloud environments, eliminating translation between them. Siemens has also worked with Sony on a headset designed specifically for mechanical design, allowing engineers to interact collaboratively with live 3D CAD data at true scale rather than with a translated visualization. The digital thread ties these capabilities to a lifecycle spanning early concept, manufacturing, maintenance and warranty. Siemens says connected workflows are already used across products ranging from consumer electronics to commercial aircraft. The article’s overall argument is that preserving engineering relationships and data continuity across tools and disciplines can remove administrative obstacles to product development. Its account remains primarily a vendor perspective: current integration and modeling capabilities are described alongside AI ambitions, but independent validation, detailed implementation requirements and measured business outcomes are not provided.

Cadence · Oct 23, 2026

AI in Product Development · CAD, CAE & Simulation

Cadence (Nasdaq: CDNS) today announced the availability of a sovereign AI data center in Munich that brings together Cadence® OnCloud, NVIDIA Vera and Grace CPU systems, Cadence Reality™ Digital Twin Platform, and the Cadence Millennium™ M2000 AI Supercomputer powered by the NVIDIA Blackwell platform. As the first EDA vendor to deliver a comprehensive sovereign AI platform for semiconductor, system and AI factory design in Europe, Cadence enables customers to design chips, advanced packaging, systems, physical AI applications and AI infrastructure while maintaining data residency within European jurisdiction. The Munich data center provides secure, European sovereign cloud infrastructure for EDA, simulation and AI workloads, combining NVIDIA accelerated computing and NVIDIA CUDA-X with Cadence’s AI-accelerated design and simulation software to support compute-intensive semiconductor, system and AI infrastructure engineering workflows. This enables European organizations to innovate while addressing growing regulatory, security and sovereignty requirements. Certified to ISO 27001, ISO 27017 and SOC 2 Type II standards, Cadence OnCloud delivers trusted cloud capabilities from its Munich deployment. The facility expands Cadence's European cloud footprint and can also support startups participating in the EuroCDP program, complementing existing European cloud resources, including leading public cloud provider infrastructure in Frankfurt. “Europe's semiconductor and system design community requires secure infrastructure that enables innovation without compromising data sovereignty,” said Paul Scannell, senior vice president of the Global Customer Success Team at Cadence. “By combining NVIDIA Vera and Grace CPU platforms with the Cadence Millennium M2000 Supercomputer and making these capabilities available through Cadence OnCloud, we are providing European engineering teams with the compute, design intelligence and flexibility needed to build the next generation of AI-driven chips and systems.” Enabling Sovereign AI Infrastructure Across Europe The Cadence Reality™ Digital Twin Platform is helping customers design, validate and optimize next-generation AI infrastructure before deployment. Working alongside the NVIDIA AI stack, including NVIDIA Omniverse libraries, the platform enables engineering teams to evaluate power delivery, cooling, airflow, thermal performance and equipment placement with engineering-grade fidelity. The Cadence Reality Digital Twin Platform supports the NVIDIA Omniverse DSX Blueprint, enabling customers to incorporate applicable DSX Blueprint capabilities into AI factory design and operational workflows. A key capability is the availability of a Cadence Reality DC Elements model for the Cadence Millennium M2000 AI Supercomputer powered by NVIDIA Blackwell GPUs. This validated digital representation enables customers to simulate and optimize AI factory and data center environments before installation, reducing deployment risk and accelerating time to operation. Cadence will also use its own Cadence Reality Digital Twin Platform within the Munich facility to optimize the design and operation of its accelerated computing infrastructure, improving energy efficiency, reducing operational risk and maximizing the sustainability of its AI computing environment. In a recent deployment, the platform helped increase data center capacity utilization from 71% to 89% while improving operational availability from 90% to 98%, enabling operators to extract more value from existing infrastructure while maintaining reliability. Together, Cadence OnCloud, the Cadence Millennium M2000 Supercomputer, Cadence Reality™ Digital Twin Platform, Cadence's AI-accelerated design and simulation portfolio, and Cadence Agentic AI technologies provide an integrated platform that allows European customers to design, build, and operate sovereign AI capabilities entirely within the EU. Partner Collaborations Cadence is working with industry leaders across the AI ecosystem to accelerate the deployment of sovereign AI infrastructure throughout Europe. At NVIDIA GTC Berlin, Cadence and NVIDIA are showcasing their collaboration to enable AI factory design and operations through the combination of NVIDIA accelerated computing, the NVIDIA Omniverse DSX Blueprint, and the Cadence Millennium M2000 AI Supercomputer, helping customers design, simulate and optimize next-generation AI factories with engineering-grade fidelity. “Chip designs and AI infrastructure are strategic assets for European industry, and sovereignty means being able to develop both while protecting the data behind them,” said Tim Costa, vice president and general manager of industrial and computational Engineering at NVIDIA. “With Cadence, we’re bringing NVIDIA accelerated computing into European cloud-based design workflows and Omniverse libraries into AI factory simulation. Companies can design the silicon and validate the infrastructure to run it—from power delivery to cooling—within a European environment built to protect their design data.” Vertiv, global leader in critical digital infrastructure, is using the Cadence Reality™ Digital Twin Platform to design, simulate and validate its modular MegaMod™ HDX AI data center. By enabling engineering teams to evaluate power, cooling and infrastructure performance before deployment, the platform helps reduce design risk and accelerate delivery of next-generation AI infrastructure. “The Cadence Reality Digital Twin Platform helps Vertiv evaluate and optimize modular AI infrastructure before deployment, giving our teams greater confidence in design decisions and reducing risk throughout the project lifecycle," said Viktor Petik, Senior Vice President, Infrastructure Solutions at Vertiv. "Combined with NVIDIA-powered AI systems and NVIDIA Omniverse technologies, the platform helps accelerate decision-making and deployment, enabling customers to bring AI infrastructure online more efficiently.” HPE is adopting the Cadence Reality Digital Twin Platform for its HPE Data Center AI Mod POD solution to design, benchmark and operate AI infrastructure, providing deeper insight into power, thermal, cooling, and operational performance. “By standardizing on the Cadence Reality Digital Twin Platform for our HPE Data Center AI Mod POD solution, including deployments built on NVIDIA accelerated computing, we can reduce deployment risk, optimize infrastructure utilization and help customers maximize the performance and efficiency of their AI solutions throughout the facility lifecycle. The University of Bristol’s Isambard-AI facility demonstrates what is possible when HPE’s modular, direct-liquid-cooled infrastructure and NVIDIA accelerated computing are brought together to deploy highly energy-efficient sovereign AI capacity at speed and supports the UK's leadership in AI innovation. Cadence digital twins can build on this foundation by providing deeper insight into power, thermal, cooling, and operational performance from planning through operations,” said Pascal Lecoq, CTO and director of Data Center Strategy Office of the CTO, HPE. France-based Nation Data Center is using the Cadence Reality Digital Twin Platform to design and validate sovereign, high-density AI infrastructure before deployment. “Our ambition is to provide sovereign and responsible digital infrastructure tailored to regional needs,” said Eric Arbaretaz, CTO of Nation Data Center. “With the Cadence Reality Digital Twin Platform, we can optimize power, cooling, and thermal performance before deployment. Cadence's validated DC Elements model for NVIDIA DGX SuperPOD with DGX GB200 systems infrastructure is particularly valuable, allowing us to accurately simulate high-density AI environments from the earliest design stages. This enables us to deliver high-performance, sustainable infrastructure tailored to our clients’ digital transformation needs.” Availability Cadence OnCloud on sovereign infrastructure is available today to European customers. Additional compute capabilities, including NVIDIA Vera and Grace and CPU systems and Cadence Millennium M2000 AI Supercomputer and Cadence Reality™ Digital Twin Platform instances, are planned as part of the ongoing expansion of the Munich data center infrastructure. About Cadence Cadence is a market leader in AI and digital twins, pioneering the application of computational software to accelerate innovation in the engineering design of silicon to systems. Our design solutions, based on Cadence’s Intelligent System Design™ strategy, are essential for the world’s leading semiconductor and systems companies to build their next-generation products from chips to full electromechanical systems that serve a wide range of markets, including hyperscale computing, mobile communications, automotive, aerospace, industrial, life sciences and robotics. In 2025, Cadence was recognized by Fortune as one of the world’s top 100 best companies to work for. Cadence solutions offer limitless opportunities—learn more at www.cadence.com. Category: Featured Cadence Newsroom 408-944-7039 newsroom@cadence.com Source: Cadence Design Systems, Inc.