AI Across The Product Lifecycle Podcast

Configuration Management is still one of the hardest problems in PLM—and this panel doesn’t sugarcoat it.In this episode of the Future of PLM Podcast, Michael Finocchiaro is joined by Rob Ferrone, Brion Carroll, Jim Brown, Oleg Shilovitsky, and Eric Schrader (Propel) to break down why BOMs still don’t match, why “single source of truth” is mostly fiction, and where AI might actually help.Key themes:Why CM ≠ BOM managementThe myth of a single version of truthVariant chaos and effectivity complexityWhy most companies still fail at adoptionAI, product memory, and the future of CMIf you work in PLM, engineering, manufacturing, or digital thread—this is a must-watch.👇 Drop your thoughts in the comments:Where is configuration management breaking down in your org?⏱️ TIMELINE00:00 – Intro + panel lineup00:09 – What is configuration management (5 definitions)03:12 – Biggest false beliefs about CM“We have a single source of truth” (we don’t)CM seen as bureaucracy vs performance leverMethodology ≠ success (adoption is the issue)06:47 – Minimum data model for CMIdentity, effectivity, baseline, traceabilityWhy data governance matters more than tools10:22 – Where CM actually lives (PLM, ERP, MES, everywhere)The “octopus problem” across systems15:12 – Hardest real-world CM problemsVariant management = BOM chaosEffectivity vs configuration confusionSoftware + firmware breaking traditional models21:53 – Debate: Effectivity (date vs serial vs lot)Why “it depends” is unavoidableSafety vs cost trade-offs24:09 – Configuration rules debate150% BOM vs model-based approachesWhy rules drift over time26:10 – Digital thread reality checkWhy duplication is inevitableImportance of product identity30:09 – As-designed vs as-built vs as-maintainedWhere control breaks down (hint: service)Why “as maintained” is the weakest link39:38 – AI in configuration managementChange impact analysisData structure vs AI hype“AI is useless without governed data”48:55 – When is the ChatGPT moment for PLM?Simplicity vs complexityPeople problem vs technology problemProduct-as-agent concept59:10 – Final thoughtsData governance as the core issueWhy we’re still having the same debates after 20 years🎯 Key TakeawaysThere is no single source of truth—only closest approximationsVariant + effectivity = core chaos engineCM failure is mostly organizational, not technicalAI will help—but only if data is structured and governedThe real frontier: making CM consumable across the enterprise📢 Follow / ConnectLinkedIn: Michael FinocchiaroMore content: DemystifyingPLM.comEvents: Threaded! Conference Series

The conversation covers the early adoption of AI in business, the impact of AI on software development, the integration of AI across business functions, the changing landscape of product development, and the role of AI in digital transformation in manufacturing. It also discusses the challenges and opportunities in implementing AI, the importance of grounded AI decisions, and the future of AI in manufacturing.TakeawaysEarly adoption of AI in businessImpact of AI on software developmentChapters00:00 Introduction to Maintain X and Machine Metrics05:19 AI Integration Across Business Functions11:30 Advice for Younger Generation in AI Era16:50 User Interaction with AI in Software22:04 Future of AI in Manufacturing27:40 Digital Maturity in Manufacturing33:09 Upcoming Trade Shows and Events

The conversation delves into the introduction and comparison of OPC UA and MQTT, the genesis and impact of MQTT on industrial automation, the purpose and evolution of OPC, the complementarity of MQTT and OPC UA, the role of MQTT in industrial data publishing, the importance of open standards and security, and the future of AI and adapting to new technology.TakeawaysMQTT and OPC UA are complementary protocolsAI needs data to be effectiveChapters00:00 Introduction to OPC UA and MQTT05:47 MQTT and OPC UA Complementarity11:11 The Role of MQTT in Industrial Data Publishing16:11 The Importance of Open Standards and Security32:35 Adapting to New Technology and the Future of AI

The conversation delves into the initial skepticism of AI and its eventual impact, the integration of AI in development, the touch points of AI in Inductive Automation, the challenges and limitations of LLMs, and the digital maturity and customer adoption of AI in the industry.TakeawaysInitial skepticism of AI gave way to its impactful integration in development.AI touch points in Inductive Automation and the challenges of LLMs highlight the evolving landscape of AI in the industry.Chapters00:00 Initial Skepticism and AI Impact06:22 AI Touch Points in Inductive Automation13:31 Digital Maturity and Customer Adoption

The conversation delves into the impact of AI on manufacturing, addressing initial skepticism, continuous transformation, and the progression of digital maturity in the industry. It also explores the role of AI in product development, code development, management, decision-making, and creativity. The discussion highlights the integration of AI in Litmus, its impact on manufacturing, and the need for continuous transformation in the industry.TakeawaysAI Adoption in ManufacturingContinuous TransformationChapters00:00 The Power of Live Demonstrations07:35 AI's Role in Code Development13:14 Addressing AI Anxiety18:53 AI Integration in Litmus25:27 AI's Role in Democratizing Technology31:36 Continuous Transformation in Manufacturing37:05 Progression of Digital Maturity

The conversation covers the impact of AI on manufacturing and the digital maturity of the industry. It delves into the use of AI in development, infrastructure management, and manufacturing transformation, as well as the future of AI in industry. Additionally, it explores the concept of digital maturity in manufacturing and the aspirations of customers to advance in this area.TakeawaysAI Impact on ManufacturingDigital Maturity in IndustryChapters00:00 Digital Maturity in Manufacturing

The conversation covers the evolution of AI, skepticism and progress, AI adoption in industry operations, AI in software development, AI in hiring and sales operations, AI in TD Engine, AI in Process Forge, AI data analysis and prediction, the future of TD Engine, and digital maturity and industry transformation. The takeaways include AI adoption in industry and the impact of AI on process automation.TakeawaysAI Adoption in IndustryAI Impact on Process AutomationChapters00:00 The Evolution of AI06:48 AI in Hiring and Sales Operations12:37 The Future of TD Engine

From CAD chaos to clean engineering context.What happens when two founders attack two of hardware engineering’s most stubborn bottlenecks: manufacturing documentation and requirements management?In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Chris Barton, Co-Founder and CTO of Drafter, and Janis Vavere of Trace.Space about what AI is actually changing in engineering right now, and what still demands deterministic precision, human review, and trust. They get into why legacy tools are failing modern teams, where AI is already delivering real value, why requirements are not going away, and how the first true “OpenAI moment” for engineering may be much closer than most people think. Timings00:00 Introduction to Chris Barton and Janis Vavere00:29 Chris Barton on Drafter and the manufacturing documentation problem01:04 Janis Vavere on Trace.Space, requirements engineering, and why the category stopped innovating02:50 The 2022 OpenAI moment: bullishness, skepticism, and early experiments05:38 Why engineering AI was not good enough three years ago05:40 Chris on precision, determinism, and why hallucinations are unacceptable in engineering drawings07:03 How AI changed software development inside Drafter09:02 How Trace.Space engineers went from skepticism to heavy AI usage11:10 Where AI is visible inside the product versus buried in the stack13:53 Deterministic outputs, human review, and reliable AI for engineering15:54 Whether startups like Drafter and Trace.Space should build their own models18:18 How these startups coexist with and challenge Siemens, PTC, and Dassault19:14 Chris on the “gray area” between CAD and manufacturing where work still runs on PDFs, email, and spreadsheets20:32 Drafter’s 2D strategy and why 2D drawings still dominate the physical world22:14 Janis on agentic iteration between requirements, drawings, and simulation23:17 Why legacy requirements tools are frustrating modern engineering teams25:47 Are the founders more bullish now than they were four years ago?28:07 Will AI collapse roles across design, manufacturing, and simulation?28:54 Do requirements still matter in a world of trade studies and optimization?30:16 Chris on first-principles engineering, design intent, and why requirements do not disappear31:21 Infinite design space exploration and what AI is unlocking32:39 Advice for younger engineers: where to work and how to stay relevant35:45 Audience Q&A: does clear design intent require explicit functional specification?36:43 Have we had the OpenAI moment for engineering yet?38:49 Why AI-native tools are exposing just how far behind legacy engineering software is41:09 Customer digital maturity: from Excel-and-email workflows to agent-first engineering44:22 Does adopting one AI-native tool trigger broader digital transformation?46:12 The customer epiphany moment after using Trace.Space or Drafter46:47 Where to meet Janis and Chris at upcoming events48:00 Closing remarks and AWS sponsorship mention

The conversation explores the impact of AI, particularly LLMs, on engineering design and development. It delves into the integration of AI into engineering workflows, the empowerment of engineers through AI, and the potential for AI to transform engineering disciplines. Both Neural Concept and nTop platforms are discussed in terms of their AI integration and impact on engineering workflows.TakeawaysAI as an accelerator in engineeringAI empowerment in engineeringChapters00:00 Introductions and Company Overview06:41 AI as an Accelerator in Engineering13:13 AI Empowerment in Engineering20:44 Integration of Disciplines through AI30:25 Integration of Tools and Platforms37:40 Role of AI in Engineering

The conversation explores the impact of AI in two distinct industries: maritime vessel design and medical imaging. Both Shahroz Khan and Roger Johnston discuss the integration of AI into their respective fields, highlighting the specific challenges and opportunities they have encountered. They also delve into the regulatory framework and the role of AI in the development process. The conversation delves into the application of AI in medical and maritime industries, highlighting the challenges and opportunities for digital transformation. It also explores the impact of AI on job prospects for younger generations and the potential for AI-driven innovation in both sectors.TakeawaysNarrow focus on specific markets allows for deep integration of AI into industry-specific challengesAI has revolutionized the development process in both maritime vessel design and medical imaging, leading to significant advancementsRegulatory frameworks play a crucial role in shaping the deployment and use of AI in these industries AI's role in medical and maritime industriesChallenges and opportunities for digital transformationImpact of AI on job prospects for younger generationsChapters00:00 Intrinsic Context and Specificity in AI Models43:36 Digital Maturity in Healthcare and Maritime Industry51:12 Impact of AI on Customer Transformation

The conversation covers the impact of AI on code development and digital maturity in manufacturing. It delves into the skepticism and adoption of AI, real-time intelligence layer in manufacturing, AI's role in Node-RED flows, its impact on software development, development processes, agile development, continuous deployment, the OpenAI moment in manufacturing, challenges with current protocols, safety and auditing in manufacturing, digital maturity and adoption of FlowFuse, automation and robotics in manufacturing, and risk and uniqueness in startups.TakeawaysAI's Impact on Code DevelopmentDigital Maturity in ManufacturingChapters00:00 Introduction to Prove It Event06:01 Real-time Intelligence Layer in Manufacturing11:01 AI and Agile Development17:25 Challenges with Current Protocols in Manufacturing22:28 Automation and Robotics in Manufacturing

The conversation covers topics related to industry 4.0 innovation, product development and acceleration, as well as the adoption of AI technology. It also delves into the challenges and collaboration in the industry, along with impressive presentations at ICC and the importance of innovative technology and open standards.TakeawaysIndustry 4.0 innovationProduct development and accelerationAI technology adoptionChapters00:00 Challenges and Collaboration in the Industry

The conversation covers the evolution of AI systems from expert systems to LLMs, the application of AI in engineering and manufacturing, and the data maturity gaps in industries adopting AI. It also delves into the challenges of AI implementation, cultural transformation for automation, and the future of AI in engineering. The discussion highlights the impact of AI on competitive edge, the need for building internal agents, and the potential of AI in engineering and manufacturing.TakeawaysEvolution of AI from expert systems to LLMsApplication of AI in engineering and manufacturingData maturity gaps in industries adopting AIChapters00:00 Evolution of AI Systems09:42 AI Implementation Challenges20:29 Cultural Transformation for Automation27:38 Future of AI in Engineering

The conversation explores the experiences of mechanical engineers in the field of CAD and AI, discussing the evolution of engineering practices, the impact of AI on design processes, and the integration of AI tools within CAD systems. The conversation also delves into the challenges and opportunities presented by AI for mechanical engineers, as well as the potential for AI to capture and preserve critical engineering knowledge. The conversation delves into the role of AI in capturing knowledge, the need for data, and the localization of AI within companies. It also explores the potential impact of AI on job roles and the importance of human expertise in mechanical engineering. The discussion emphasizes the need for skepticism and validation when using AI in engineering processes.TakeawaysAI's impact on design processesEvolution of engineering practicesChallenges and opportunities for mechanical engineers in AIIntegration of AI tools within CAD systemsAI as a means of capturing and preserving critical engineering knowledge AI's role in capturing knowledgeThe need for skepticism and validation when using AI in engineering processesChapters00:00 Introduction and Engineer Introductions13:48 The Role of AI in Mechanical Engineering19:52 AI Integration in CAD Tools25:28 AI Tools and Predictive Operations31:05 AI and Knowledge Capture38:04 AI's Impact on Manufacturing and Design Processes44:43 Internal Memory Trained AIs and CAE Influence49:47 Human Expertise in Mechanical Engineering and AI

The conversation explores the use of AI across the product lifecycle management process, focusing on the experiences of Karan Talati and Michael Corr. They discuss their perceptions of AI, its application in product development, and its impact on the manufacturing industry. The conversation also delves into the evolution of AI tools and their role in achieving deterministic results in manufacturing. The conversation explores the application of AI in manufacturing and hardware, focusing on the challenges, opportunities, and cultural shifts associated with the adoption of AI technologies. It delves into the role of AI in change orders, the probabilistic nature of manufacturing, the importance of building trust with end customers, and the concept of augmentation in manufacturing.TakeawaysAI's evolution from speculative technology to a must-have tool for startupsThe use of AI in product development and its impact on productivity and efficiency AI's role in change ordersThe probabilistic nature of manufacturingChapters00:00 AI in Manufacturing and Deterministic Results30:55 Building Trust and UX in AI for Manufacturing

The podcast features a discussion with the CEO and CTO of Quanscient, a Finnish quantum computing startup, covering topics such as quantum computing, its relationship with AI, and the future of both technologies. The conversation delves into the nature of quantum computing, its applications, the use of AI in quantum development, and the future roadmap for quantum computing. The conversation covers topics such as quantum computing, its impact on engineering, digital maturity, quantum-safe encryption, and the future of engineering software. It also explores the potential of quantum computing in solving complex problems and the challenges of data security in the quantum era.TakeawaysQuantum computing is a different computing paradigm from classical computing, utilizing qubits and superposition to solve exponentially larger problems.AI and quantum computing are complementary, with AI methods being used to shorten quantum programs and reduce noise in quantum computing.The future of quantum computing looks promising, with companies like IonQ promising several millions of qubits by the end of 2029, potentially leading to full-blown quantum advantages for various fields. Quantum computing has the potential to revolutionize engineering and design processes.The development of quantum-safe encryption methods is crucial for data security in the quantum era.Chapters00:00 The Future of Quantum Computing and AI35:11 Digital Maturity and Data Readiness41:24 Quantum Computing and Power Efficiency48:13 European Sovereignty and Quantum Computing54:11 Impact of Quanscient on Digital Maturity

The conversation covers the integration of AI in the development process, its application in mechanical engineering, and its role in decision-making. The discussion explores the use of AI in the development of Leo AI and OpenBOM, its potential in mechanical engineering, and the impact on decision-making processes.TakeawaysAI in development processAI for mechanical engineeringAI and decision-makingChapters00:00 Integration of AI in Development

The conversation explores the application of AI in the product lifecycle, focusing on design and manufacturing. It delves into the challenges of integrating AI into existing workflows and the development of custom AI models for specific engineering applications. The conversation delves into the challenges and opportunities of AI integration in engineering, emphasizing the importance of empathy, problem-solving, and human-AI collaboration. It also addresses the impact of AI on job markets and the need for adaptability and skill development.TakeawaysAI's impact on design and manufacturingCustom AI models for specific engineering applications Human-AI collaborationAdaptability in job marketsChapters00:00 Introduction to AI in Product Lifecycle08:12 Early Experiences with AI13:33 AI in Code Development24:14 Training Custom AI Models31:20 Challenges in AI for CAD55:55 The Future of Engineering Jobs

The conversation begins with introductions and backgrounds of the guests, followed by a discussion on initial perspectives on AI in engineering software. The impact of AI on engineering collaboration and workflow automation is then explored, along with the integration of AI in engineering software and user interaction. The conversation concludes with a discussion on the future of engineering software and AI integration. The conversation delves into the challenges and opportunities presented by AI in engineering. It explores the importance of root cause analysis, collaboration, and the need to avoid recurring mistakes. The potential of vision language models and the impact of AI on digital maturity in engineering organizations are also discussed. Furthermore, the urgency of centralized data repositories and the impact of AI on engineering careers are highlighted.TakeawaysAI as a Tool for Solving Customer ChallengesAI's Evolution in Engineering SoftwareThe Role of AI in Engineering Collaboration AI in EngineeringChallenges in AI AdoptionChapters00:00 Introductions and Backgrounds of the Guests08:19 AI's Impact on Engineering Collaboration and Workflow Automation15:43 Integration of AI in Engineering Software and User Interaction22:21 The Future of Engineering Software and AI Integration30:36 Root Cause Analysis and Collaboration in Engineering39:04 Digital Maturity in Engineering Organizations48:05 Urgency of Centralized Data Repositories53:20 Impact of AI on Engineering Careers

The conversation covers the adoption and impact of AI in IoT, AI, and industrial environments, as well as its role in servitization. It also delves into the development of AI models, IP creation, and the use of AI for quality control and reporting in an industrial context. The conversation delves into the role of AI as a tool for knowledge capture and its impact on industry, digital maturity, and societal transformation. It explores the competitive advantage of AI, the evolution of AI and digital maturity, the ripple effect of AI implementation, and the societal impact of AI.TakeawaysAI in Product DevelopmentServitization and AIAI in Industrial Environment AI as a Tool for Knowledge CaptureSocietal Impact of AIChapters00:00 AI in Industrial Environment and Quality Control42:11 Competitive Advantage of AI in Industry54:09 Ripple Effect of AI Implementation