AI for Engineering Is Leaving the Demo Phase

AI for Engineering Is Leaving the Demo Phase

Text-to-CAD. Autonomous simulation. Agentic workflows. AI copilots for PLM. Engineering teams “10x faster.” Most of it still sounds like science fiction. But what happens when you put two founders building real AI-native engineering software in the same conversation? In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Pradyut, co-founder of Bild, and Martin Bielicki, co-founder and CEO of Bench, about what AI is actually changing in engineering software, CAD data, simulation, product development, and manufacturing workflows. Bild is building CAD data management that connects engineering to manufacturing. Bench is building an AI orchestration layer across CAD, simulation, PLM, and beyond. The discussion cuts through the hype: AI is already changing how startups code. QA and validation are becoming the bottleneck. Prompting matters less than context. Frontier model cost is becoming a real burn-rate issue. And engineering AI will not move as fast as software AI because CAD, simulation, manufacturing, sourcing, and PLM are different technical worlds. The real unlock is not “AI replacing engineers.” Timeline 00:00 – Introduction: Bild, Bench, and AI across engineering 00:29 – Pradyut introduces Bild 00:54 – Martin introduces Bench 01:16 – The OpenAI moment 01:55 – Bench was created because of the LLM breakthrough 02:25 – Bild’s early exposure to DALL·E 03:33 – How AI changed startup coding 04:01 – Cursor, Claude Code, Slack, Graphite, and same-day delivery 05:45 – Multi-agent development 06:37 – Why “looking good” is becoming commoditized 07:28 – Why old software stacks limit AI innovation 08:21 – Prompting vs context 09:53 – From prompts to loops 10:40 – Frontier LLM costs 11:20 – Token costs as the new AWS-style shift 12:15 – AI spend caps and productivity measurement 13:45 – Cheaper models and model routing 14:39 – Right model, right task 15:39 – AI and engineering org structure 16:19 – QA, validation, and human-in-the-loop checks 17:58 – How AI may reorganize hardware teams 18:50 – Multi-agent coding conflicts 21:16 – Where AI lives inside the product stack 21:42 – Bench: AI for context, planning, and judgment 22:43 – Bild: opt-in AI for CAD data and IP boundaries 24:19 – When will engineering have its OpenAI moment? 25:04 – Why engineering AI evolves use case by use case 26:35 – Faster adoption in consumer products? 27:04 – From text-to-CAD to DFM and manufacturability 29:09 – The coming CDFAM AI demo wave 30:05 – Advice for young engineers 30:43 – Don’t compete with agents. Build differentiated skills. 33:05 – Creativity roles and AI in physical sciences 35:06 – Why top engineers become more valuable 35:57 – Digital transformation reality check 37:21 – Prints, redlines, and physical sign-offs are still alive 39:31 – Can startups move faster than legacy vendors? 40:10 – Big OEMs asking for AI engineering visions 41:15 – Buying AI vs buying value 42:50 – Why transformation programs route back to incumbents 43:37 – Build vs Windchill 45:30 – Startup visibility vs legacy vendors 47:56 – Capability checkboxes vs real user experience 49:13 – AI agents for CAD and CAE workflows 49:33 – End-to-end orchestration and organizational readiness 51:49 – Keeping skilled engineers in the loop 52:26 – Trust but verify for hardware AI 53:39 – Where to meet Bild and Bench 55:31 – Closing remarks Featuring Pradyut of Bild and Martin Bielicki of Bench. Hosted by Michael Finocchiaro. #AI #EngineeringSoftware #CAD #PLM #Simulation #Manufacturing #DigitalThread #IndustrialAI #HardwareEngineering #Startups

Who really owns the Bill of Materials?

BOM Wars, Part 2: Why Engineering, Manufacturing, ERP, MES, Service, and AI Still Can’t Agree The BOM debate is back. And somehow, it got even more dangerous. In this Future of PLM panel, Michael Finocchiaro brings together Christine Longwell, Gus Quade, Brion Carroll, Pat Hillberg, David Schultz, and Oleg Shilovitsky for a high-energy debate on one of the most persistent fractures in product lifecycle management: Who really owns the Bill of Materials? Engineering says the EBOM defines the product. Manufacturing says the MBOM defines what can actually be built. ERP says the operational BOM is what matters. MES wants execution context. Service wants the as-maintained truth. And AI? AI is useless unless all of this data is normalized, contextualized, and connected. This episode goes deep into EBOM vs MBOM, recipes vs discrete manufacturing, fashion vs aerospace, service BOMs, circular economy, Conway’s Law, ISA-95, product memory, data governance, and why every “single source of truth” eventually collides with organizational reality. The conclusion? The BOM is not just a list of parts. It is a battleground between systems, silos, budgets, ownership, and the future of industrial AI. Timeline 00:00 – Introduction: BOM Wars, Part 2 00:54 – Christine Longwell and Gus Quade join the panel 02:20 – Autodesk’s MaintainX acquisition and service implications 03:14 – Jörg Fischer’s provocation: “The BOM doesn’t exist” 04:30 – ERP BOM vs MES vs MBOM: where does manufacturing truth live? 05:31 – Engineering defines the product, manufacturing defines the action 08:04 – Why BOM logic changes by industry 09:50 – Fashion, fabric, tech packs, suppliers, and PLM 12:29 – Should EBOM, MBOM, as-built, as-shipped, and as-serviced live in one system? 14:55 – Product memory and algorithmic BOM transformation 16:00 – Conway’s Law: why BOM structures mirror organizations 19:21 – Can product memory connect engineering and manufacturing logic? 21:02 – Is the MBOM a separate object or just a different view? 22:33 – Digital thread, service BOMs, and lifecycle responsibility 24:44 – TWA 800, aircraft traceability, and why as-built data matters 27:30 – Why silos exist because organizations exist in silos 28:29 – AI orchestration across PLM, MES, ERP, and service 30:36 – Service BOM, software BOM, spare parts, and terminology chaos 32:31 – Why AI needs normalized data before it can add value 33:58 – Conway’s Law and the limits of database-driven transformation 37:55 – EBOM vs MBOM through the CAD and manufacturing lens 39:31 – When does a product become “real”? 41:47 – CIOs, data governance, and who should own cross-silo truth 44:38 – Autodesk’s data model approach and the unified product record 45:06 – PTC Orbit, Jetstream, and the race toward digital thread platforms 48:36 – Master data models, ontologies, and common exchange standards 50:32 – Closing question: what BOM belief will be wrong in five years? 51:27 – Oleg: data misalignment and unit-of-measure disasters 54:44 – Gus: CAD-optimized vs ERP-optimized structures will stop being binary 57:37 – David: the danger of returning to point-to-point integrations 59:14 – Brion: role-based UX on top of shared product ontology 61:16 – Pat: digital thread will eventually collapse EBOM/MBOM boundaries 62:50 – Christine: BOM is product definition, not just a parts list 63:57 – Do we need BOM Wars Part 3? Featuring: Christine Longwell Gus Quade Brion Carroll Pat Hillberg David Schultz Oleg Shilovitsky Hosted by Michael Finocchiaro #PLM #BOM #EBOM #MBOM #DigitalThread #Manufacturing #ERP #MES #EngineeringSoftware #AI #ProductLifecycleManagement #FutureOfPLM

Aras enters the Leader's Quadrant!

🚨 PLM just had a major market signal. For the first time since 2008, Gartner has published a Magic Quadrant for PLM — and Aras is now positioned alongside the traditional PLM giants. In this breaking-news episode of AI Across the Product Lifecycle, I sit down with Josh Epstein, CMO of Aras, to unpack what this means for the PLM market, why digital thread has become central to enterprise software strategy, and why “governed engineering AI” may be the real battleground for the next generation of product development platforms. We discuss why PLM has become too important for analysts to ignore, how Aras positions itself differently from Siemens, Dassault Systèmes, and PTC, and why AI in engineering cannot just be another copilot bolted onto messy enterprise data. The key question: Can AI transform engineering without a governed, explainable digital thread underneath it? Josh also goes deep on Aras Innovator Edge AI, Thread RAG, product memory, context graphs, lifecycle-aware AI agents, and what the engineer’s workday could look like when PLM starts decomposing into governed micro-experiences and agent-driven workflows. If you care about PLM, digital thread, engineering AI, enterprise software, or the future of product development, this one matters. ⏱ Timeline 00:00 — Breaking news: Gartner brings back the PLM Magic Quadrant 00:36 — Why did Gartner wait so long after 2008? 02:06 — Has the PLM market fundamentally changed? 04:28 — Why PLM is more complex than ERP or CRM 06:05 — Aras vs. the “Big Three” PLM incumbents 06:37 — Why CAD-agnostic PLM may now be an advantage 07:21 — Governed engineering AI vs. generic AI hype 09:37 — Trust, governance, observability, and explainability 11:34 — Why AI needs the digital thread to be actionable 12:45 — PLM data complexity: versions, effectivity, access, context 15:05 — How to market AI in 2026 without overpromising 15:53 — Aras Innovator Edge AI, Thread RAG, and workflow agents 17:08 — Product memory, context graphs, and decision traces 19:08 — Does Gartner validation change the sales conversation? 21:17 — Is PLM still the right category name? 23:50 — Cognitive digital thread vs. product memory 26:00 — What does an engineer’s day look like in three years? 27:00 — Adaptive PLM, micro-experiences, and agent-driven work 29:30 — Why PLM AI cannot just be dumped into a data lake 31:30 — The physical-world constraint: “close enough” is not enough 32:00 — Has PLM had its OpenAI moment yet? 🎯 Subscribe for more conversations on AI, PLM, CAD, manufacturing software, digital thread, and the next generation of engineering platforms. 💬 Comment THREAD if you want more deep dives on PLM, governed AI, and the engineering software startups reshaping this market. #PLM #DigitalThread #EngineeringAI #Aras #Gartner #MagicQuadrant #ProductLifecycleManagement #AI #EnterpriseAI #Manufacturing #CAD #PDM #ProductDevelopment #IndustrialAI #AgenticAI #AIEngineering #DemystifyingPLM #ThreadMoat

The Future of PLM Is Human? AI, Trust, Community & the Share PLM Summit 2026 Debate

What happens when some of the most respected voices in PLM gather in a Spanish vineyard to discuss AI, digital transformation, trust, community, and the future of engineering? In this special Share PLM Summit 2026 edition of The Future of PLM Podcast, host Michael Finocchiaro is joined by Jos Voskuil, Oleg Shilovitsky, Rob Ferrone, Patrick Hillberg, Nina Dar, and Maria Morris for a candid, unscripted discussion about the ideas that emerged from one of the industry’s most unique events.   The conversation explores why the human side of PLM remains the hardest part of transformation, whether AI will fundamentally reshape consulting and knowledge work, how organizations build trust during digital change, and why community may be becoming more important than technology itself. From AI adoption and organizational change to conference design and the future of professional expertise, this episode offers practical insights and thought-provoking perspectives from some of the industry’s most experienced practitioners. TOPICS COVERED • The evolution of Share PLM Summit and its human-centered approach • AI’s impact on engineering, consulting, and PLM careers • Why trust may be the real ROI of conferences • Lessons from successful and unsuccessful PLM transformations • Human adoption versus technical implementation • Digital transformation beyond software deployment • The future of work in an AI-driven world • Community, collaboration, and knowledge sharing TIMELINE 00:00 Welcome & introductions 01:20 Why Share PLM Summit feels different 03:30 Breaking away from traditional PLM conferences 05:45 Why attendees travel across continents to attend 07:35 PLM as a people-centered discipline 09:50 AI, digital overload, and human connection 12:40 Measuring conference ROI beyond leads and sales 15:10 Most impactful presentations from the summit 20:05 Data, AI, and the Gentelligence perspective 22:10 Helena Haapio’s keynote and the future of work 24:50 Will AI replace consulting and expertise? 30:05 AI, critical thinking, and engineering risk 31:10 Sponsors, trust, and community building 36:00 Workshops, learning, and audience engagement 42:20 Sustainability and digital product passports 48:20 The Share Nest initiative 51:55 The future of conferences and professional development 58:20 Trust as the new business currency 01:01:00 Community, networking, and collaboration 01:03:40 The value of disagreement and debate 01:05:00 One word that defines Share PLM Summit 2026 01:07:00 Closing thoughts #PLM #AI #DigitalTransformation #Engineering #Manufacturing #Industry40 #DigitalThread #DigitalTwin #ProductLifecycleManagement #IndustrialAI #FutureOfPLM #SharePLM #EngineeringLeadership #SystemsEngineering #Innovation #TechnologyLeadership

When AI Meets Sales, Support & Supply Chain: Omnae & Bardin AI

AI in manufacturing does not fail because the demo is bad. It fails when the answer cannot be trusted. In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Fay Goldstein, Co-Founder and CEO of Bardin AI, and Scott Lionello, Co-Founder and CPO of Omnae Technologies, about where industrial AI is really going: beyond chatbots, beyond copilots, and into the operational workflows that actually run manufacturing businesses. Bardin AI is building an application engineer for industrial automation sales and support teams, helping them answer complex technical questions without escalating everything to senior engineers. Omnae is building supply chain collaboration software that allows AI agents to operate safely across real suppliers, buyers, orders, invoices, and messy enterprise data.   The conversation goes straight into the hard parts of industrial AI: trust, auditability, determinism, human-in-the-loop workflows, knowledge graphs, API costs, token burn, procurement risk, sales engineering bottlenecks, and why “just add a chatbot” is not enough when mistakes touch contracts, general ledgers, supply commitments, or customer trust. Fay and Scott also discuss how AI is changing startup operations and software development, why young professionals need to show AI fluency rather than fear AI replacement, and why mid-market manufacturers may adopt practical AI faster than large enterprises waiting for top-down transformation programs. The big takeaway: the next wave of industrial AI will not be about flashy demos. It will be about operational relief. Fewer escalations. Faster quoting. Cleaner supplier collaboration. Better support workflows. Safer automation. More trust in the decisions AI helps make. This is a grounded, founder-level conversation about how AI is moving into the less glamorous but highly valuable parts of the product lifecycle: sales, support, procurement, supply chain, and the industrial back office. Topics covered: industrial AI, agentic AI, supply chain AI, procurement, pre-sales engineering, industrial automation, knowledge graphs, AI trust, human-in-the-loop workflows, manufacturing software, digital transformation, enterprise AI, startup innovation, and the future of AI across the product lifecycle.