AI Across The Product Lifecycle Podcast

What happens when AI, virtual reality, and spatial computing move beyond demos and start reshaping real engineering work?In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Jay Wright, Co-Founder and CEO of Campfire, and Oluwaseyi “Shay” Sosanya, Co-Founder and CEO of Gravity Sketch, about the future of immersive engineering workflows.This is not a “metaverse” conversation. It is about what spatial tools can actually do for product development, design reviews, manufacturing validation, training, collaboration, and digital transformation.Jay explains why AI is becoming a first-class user inside Campfire, acting almost like another participant in a 3D workspace. Shay breaks down why Gravity Sketch keeps humans at the center of the design process while using AI to remove friction, speed iteration, and help teams communicate better.The conversation covers the hard parts too: why LLMs still struggle with geometry, why industrial companies remain cautious about cloud and AI adoption, why employees are already using AI tools outside official policy, and why the next breakthrough in engineering may not be AI replacing CAD, but AI controlling and accelerating the tools engineers already use.For anyone working in CAD, PLM, industrial AI, digital thread, manufacturing, design, or engineering software, this is a sharp look at where spatial computing is actually useful and where the hype still needs to become workflow value.Featuring:Jay Wright, Co-Founder & CEO, CampfireOluwaseyi “Shay” Sosanya, Co-Founder & CEO, Gravity SketchHost: Michael Finocchiaro, AI Across the Product LifecycleTranscript source:  Timeline00:00 Welcome and guest introductions03:05 Jay Wright on being bullish about AI after ChatGPT04:33 Shay Sosanya on cautious optimism and the speed of AI progress07:06 Why 3D geometry is harder for AI than language08:42 AI capabilities are moving faster than expected10:07 How Gravity Sketch adopted AI in software development12:27 Campfire’s AI-assisted development workflow13:32 AI agents in meetings, code, and product workflows16:11 Using AI with existing 3D assets, BOMs, documents, and legacy data18:26 Campfire’s spatial workflows for engineering, training, and sales20:02 Where AI sits in the software stack20:28 Campfire’s spatial agent as a first-class user21:46 Gravity Sketch’s human-first approach to AI in spatial design23:36 Foundation models, 3D generation, and geometry engines25:29 AI cost, IP protection, customer data, and bring-your-own-LLM models28:00 Has engineering had its ChatGPT moment yet?29:05 Why physical product development will see staged AI adoption31:17 The engineering-to-manufacturing gap32:13 Simulating manufacturing workflows before production34:12 AI connectors, Blender, Fusion 360, and tool control35:18 Advice for young engineers worried about AI39:41 Making real products, not just AI-generated concepts40:00 Digital maturity in industrial companies41:21 Why many manufacturers remain at low digital maturity42:31 Headsets, cloud, InfoSec, and adoption barriers43:39 Employees are already using AI and immersive tools informally46:57 Can agile startups move industrial customers faster than incumbents?48:17 Campfire on solving workflows rather than selling AI novelty50:29 Gravity Sketch on value, workflow depth, and avoiding AI hype53:09 Where to see Campfire and Gravity Sketch next56:12 Closing thoughts

Riverside Event TitlePhysics Has a ChatGPT Moment: AI, Simulation, and the Future of EngineeringWhat happens when AI stops guessing and starts solving physics?In this episode of AI Across The Product Lifecycle, I’m joined by Hardik Kabaria, co-founder and CFO of Vinci, and Andy Fine of the Fine Physics Consortium, for a sharp discussion on one of the biggest shifts in engineering software: AI-native physics simulation.Vinci is building a physics intelligence layer: a foundation model for physics designed to answer real engineering questions around heat transfer, thermo-mechanical deformation, high-fidelity simulation, and manufacturing-resolution analysis. Hardik says Vinci is already deployed with tier-one hardware companies and can run simulations from hundreds of millions to over a trillion degrees of freedom.  This is not vague AI hype.We dig into what makes AI simulation credible, why deterministic physics matters, how engineers can validate results, and why thermal problems are becoming mission-critical across semiconductors, electronics, batteries, EVs, data centers, robotics, and advanced manufacturing.If your product generates heat, deforms under load, consumes power, or depends on simulation to avoid expensive failures, this conversation matters.Timeline00:00 — Introduction: Vinci, Fine Physics Consortium, and the “OpenAI moment” for simulation01:11 — What is physics intelligence?02:18 — Why physics is universal and governed by differential equations03:08 — Physics-based AI vs. surrogate models04:01 — What makes a physics foundation model credible?06:51 — Why business value beats white papers08:33 — Where Vinci fits in the engineering workflow10:16 — Heat transfer, fluid dynamics, and choosing the right wedge use case11:14 — Vinci’s focus: semiconductor and electronics thermal problems13:23 — Thermo-mechanical deformation and why materials warp14:49 — Multi-physics simulation as a long-standing engineering holy grail16:06 — Yield, reliability, and manufacturing risk in electronics17:04 — ROI: faster design loops and thousands of analyses per day19:23 — Uncertainty, validation, and trust in AI simulation20:08 — Training on 45TB of physics simulation data21:47 — Residual norms and transparency at inference time24:42 — 300 million to 1.2 trillion degrees of freedom25:51 — GPU requirements and why Vinci is built for modern hardware27:09 — Quantum computing, GPUs, and future scalability30:22 — Wedge use cases: chips, boards, servers, batteries, defense, robotics, steel plants31:45 — Who buys AI-native simulation software?33:50 — Why thermal engineers are Vinci’s first target users35:06 — Power, cooling, throttling, and data center energy constraints36:25 — What throttling means in chips, EVs, and thermal runaway scenarios39:58 — Deployment, IP protection, Docker containers, cloud, and on-prem41:27 — How to convince skeptical engineers43:00 — Path to adoption: start with the customer’s real benchmark44:16 — What engineering leaders should do next45:47 — The physics brick in the AI factory of the future46:03 — Final debate: can there ever be one general foundation model for all physics?Join us for a practical, skeptical, deeply technical conversation about what AI can actually do for simulation, hardware design, and the next generation of engineering software.#AI #Simulation #EngineeringSoftware #PhysicsAI #DigitalThread #Semiconductors #ThermalEngineering #CAE #ProductDevelopment #AIAcrossTheProductLifecycle #TheFutureOfPLM #BetterCallFino

Riverside Event TitleProduct Memory: The Missing Layer Between PLM, Digital Thread, and AI AgentsRiverside Event DescriptionEveryone talks about the single source of truth.Then the real product decision happens in a meeting, spreadsheet, email, Teams chat, supplier exchange, or inside someone’s head.In this episode of The Future of PLM, I’m joined by Oleg Shilovitsky of OpenBOM, Rob McAveney CTO of Aras, Brion Carroll of Digital Solution Group, David Segal of TCS, and Jonathan Scott of Razorleaf for a sharp discussion on one of the most important emerging ideas in PLM and enterprise AI: Product Memory.The core question:If digital thread connects the data, what captures the reasoning?PLM manages parts, BOMs, changes, documents, requirements, and workflows. But it often misses the “why” behind decisions: assumptions, rejected options, supplier constraints, manufacturing context, cost tradeoffs, effectivity logic, and informal reasoning.This discussion explores whether Product Memory becomes the next layer above PLM, ERP, MES, QMS, ALM, supplier systems, documents, and collaboration tools: a contextual, semantic, AI-ready memory of how product decisions are made across the enterprise.We cover:Can Product Memory avoid becoming another inconsistent data layer?What should be captured, and what should be filtered out?Why does eBOM-to-mBOM still break so many digital threads?How do semantics and ontology determine whether AI can trust product context?Can AI agents safely recommend or execute PLM changes?How do we capture human decision-making without scaring the humans?Timeline00:16 — Introduction: single source of truth, broken digital threads, and Product Memory03:02 — Oleg defines Product Memory beyond single source of truth and digital thread06:28 — Rob on dependency graphs and hidden context in unstructured documents08:36 — Brion on Product Memory as an “orb” fed by siloed enterprise systems11:39 — Jonathan on semantics: why “part” means different things across functions13:46 — David on Product Memory from an enterprise architecture perspective18:21 — Avoiding inconsistent data across PLM, ERP, PIM, e-commerce, and supply chain22:09 — Why engineering-to-manufacturing translation is so hard25:00 — Why engineering release is not the finish line30:05 — Missing memory: decisions in people’s heads, spreadsheets, and informal actions33:57 — Why skipping change steps can slow the enterprise down35:57 — AI agents, requirements ingestion, and asking “why” like a three-year-old39:48 — Why AI agents must document their own reasoning42:49 — Product Memory flywheel: capture, review, flow, and distribution45:35 — Industrial AI, physical AI, agentic AI, and real-time product memory48:21 — Semantic consistency, meta layers, and vetting data before Product Memory52:15 — Dependency graphs, imperfect data, and improving ontology over time55:12 — Human maturity: is the organization ready?56:56 — Where companies should start looking for missing Product Memory1:03:58 — Rob’s call to action: start capturing decision traces now1:05:03 — Closing: eBOM, mBOM, ISA-95, and semantic translationThis is not a theoretical PLM buzzword session. It is a practical debate about architecture, governance, trust, and human maturity before AI agents can operate safely inside the product lifecycle.#PLM #ProductMemory #DigitalThread #AI #AgenticAI #EngineeringSoftware #EnterpriseArchitecture #BOM #MBOM #EBOM #Manufacturing #TheFutureOfPLM #BetterCallFino

What happens when AI hits both sides of the engineering equation: design and sourcing?In this episode of AI Across the Product Lifecycle, Michael Finocchiaro sits down with Adar Hey, CEO and co-founder of Jiga, and Or Israel, CEO and co-founder of Bananaz, for a grounded discussion on where AI is actually creating value in engineering right now. Bananaz is building an AI layer on top of CAD to automate manual engineering work, while Jiga is rethinking custom part sourcing with software, supplier intelligence, and AI-enabled operations.  This is not a hype piece. The conversation gets into the real tradeoffs: where LLMs help, where deterministic workflows still matter, how engineering startups are using AI internally to ship faster, how customers think about ROI, and why security, traceability, and IP protection still make or break adoption. It also explores a bigger question: when will engineering have its true “OpenAI moment”? Adar argues adoption in physical industries takes time even when the technology is ready, while Or says the shift is already underway and could become unmistakable in 2026 to early 2027.  One of the strongest parts of the episode is the discussion around digital maturity. Both founders place many target customers around a 2 to 3 out of 5: digital enough to understand the value, but far from autonomous or agentic. From there, the discussion turns practical: how do you introduce change without breaking habits, and how do you prove business impact across engineering, manufacturing, and supply chain?    If you care about CAD copilots, sourcing automation, engineering productivity, AI in industrial software, startup execution, and the future of digital engineering, this episode is worth your time.  Timeline00:14 — Intro: Adar Hey of Jiga and Or Israel of Bananaz00:40 — What Bananaz does: AI layer on top of CAD01:24 — What Jiga does: sourcing custom parts more efficiently02:20 — Were they bullish or skeptical on AI in 2022?06:01 — How AI changed the way they build software10:50 — Token costs, burn rate, and ROI of AI tools14:22 — Where AI sits in the product stack18:00 — Off-the-shelf LLMs vs open-source models20:15 — Bring-your-own-model vs vendor-managed AI22:22 — Security, SOC 2, and protecting customer IP26:01 — Are they more bullish now than in 2022?27:28 — Who owns IP when designs are partially AI-generated?31:52 — Advice for younger engineers worried about AI replacing jobs35:49 — When will engineering get its “OpenAI moment”?40:09 — Digital maturity of current customers42:29 — Do tools like Jiga and Bananaz move the maturity needle?47:30 — Closing thoughts and where to meet the founders next        Hashtags#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #Jiga #Bananaz #AIAcrossTheProductLifecycle #BetterCallFino

What happens when engineering teams suddenly have the equivalent of 10,000 new AI coworkers?In this Day 2 Threaded Miami session, Daan Goossens of CoLab lays out a sharp version of the problem: AI can massively increase productivity, but if companies do not have the right collaboration, context, and decision-making structure in place, they will not scale output. They will scale chaos. That framing runs through the entire talk and makes this one of the clearest strategic discussions from the event. Daan explains that CoLab’s vision for the future is not about replacing engineers. It is about helping humans do more with more, pairing human judgment with AI agents and the right engineering context. His point is that the engineer remains accountable, especially in safety-critical industries, but AI can dramatically expand what teams are capable of if it is embedded responsibly. He reduces the path to scaled engineering productivity down to three ingredients: human collaboration and decision-making, strong AI agents, and relevant context and data. Miss one of those and the whole thing breaks. That is why CoLab is building around engineering collaboration first, especially design review, where teams already need to bring together multiple stakeholders, surface issues, and make decisions quickly without drowning in screenshots, PowerPoints, email chains, and Teams messages. Daan also gives a concrete look at where CoLab is going next. He shows how their design engagement system and AI reviewer, Otto, are evolving into a broader engineering operating system strategy. One especially strong example is the SimScale partnership proof of concept, where a user can request a static load analysis on a crane truss, let the AI build and review the simulation plan, run the simulation, and then bring the results back into CoLab for collaborative review alongside the rest of the design context. The broader message is clear: engineers should not have to keep jumping between disconnected tools just to understand the impact of a design decision. This is less a product demo than a thesis on where engineering software is headed. CoLab is betting that the future belongs to platforms that can combine collaboration, AI, and engineering context in one place, while partnering with the best core tools rather than trying to rebuild everything themselves. If you care about AI in engineering, design review, simulation workflows, or the emerging idea of an “engineering OS,” this episode is worth your time.#ThreadedMiami #CoLab #EngineeringAI #DesignReview #Simulation #DigitalThread #IndustrialAI #ProductDevelopment #EngineeringOS #ManufacturingTech #AI

What if the missing piece in industrial AI is not another copilot, but the underlying knowledge and orchestration layer that physical product companies still do not have?In this Day 2 Threaded Miami session, Lucy Hoag of Violet Labs makes that case from a hardware-first perspective. Her argument is that the physical world is getting dramatically more complex, multidisciplinary, and AI-enabled, while the way companies manage product data is still stuck in a much older, mechanically centered paradigm. The result is familiar: disconnected systems, fragmented context, and no reliable foundation for AI to reason over or act on. Lucy frames Violet as the knowledge and orchestration layer for the physical world. The company starts by pulling together data across the lifecycle through a large and growing set of no-code integrations spanning requirements, CAD, PLM, MES, ERP, simulation, and domain-specific aerospace tools. That data is normalized into a shared ontology so that parts, items, inventory, requirements, and related objects can be understood consistently across systems instead of remaining trapped in tool-specific silos. What makes the talk timely is her emphasis on AI readiness. Lucy is clear that generative AI and agentic workflows are exciting, but they do not work reliably if the underlying data is disconnected. Violet’s answer is not just sync for sync’s sake. It is to create the infrastructure that lets companies build reports, automate workflows, trace decisions, and ultimately expose governed, permissioned engineering data to agents through things like MCP. In her framing, AI is not the starting point. Connected context is. She also gets into the less glamorous but more important details: observability, auditability, approval logic, hybrid sync models, webhook support, and the messy reality of older engineering tools that do not behave like modern SaaS apps. That gives the presentation more credibility than a generic “single source of truth” pitch. Violet is trying to solve the boring infrastructure work that has to exist before agentic AI becomes operationally trustworthy. Another strong part of the talk is how broad the ambition is without pretending everything needs to live inside Violet’s own UI. Lucy points to chat interfaces, multi-source reports, BOM comparison, clear-to-build views, and MCP-driven custom apps as different ways users can consume the same underlying data foundation. That suggests Violet sees the future less as one monolithic interface and more as a connected data layer that other applications, agents, and teams can build on top of. This is a useful episode for anyone in PLM, systems engineering, aerospace, hardware startups, manufacturing IT, or industrial AI. It is a thoughtful argument that before the industry gets carried away with agents doing everything, it still has to solve the much older problem of fragmented engineering knowledge.#ThreadedMiami #VioletLabs #DigitalThread #PLM #SystemsEngineering #IndustrialAI #HardwareEngineering #ManufacturingTech #AgenticAI #EngineeringData #AI

What if the real weak point in the digital thread is not the software stack, but the documents people keep creating to survive around it?In this Day 2 Threaded Miami session, Addy First of Quarter20 makes that argument directly. She points out that while everyone talks about connected systems and AI in manufacturing, a huge amount of real work still happens outside those systems in PowerPoints, Word docs, PDFs, screenshots, and spreadsheets. Those documents become the human-facing interface to engineering, manufacturing, service, quality, and supply chain work — and the moment they are created, they often detach from the source of truth. That is the core problem Quarter20 is going after. Addy argues that companies can either force every worker, technician, and partner to operate directly inside enterprise systems, or they can fix the documentation problem itself. Her view is that the second path is far more realistic. Documentation is not going away, so the real opportunity is to make it dynamic, connected, and continuously updated rather than static and stale. She lays out four reasons this matters. Engineering intent often fails to reach execution cleanly. Changes do not propagate reliably once documents are manually created. Work happening through documents creates no useful traceability. And without structured, trustworthy documentation, applying reliable AI becomes much harder. Her punchline is strong: as long as humans are doing work, humans will need documents, and that means documentation has to be brought back inside the digital thread instead of treated as an afterthought. Quarter20’s answer is a human-facing collaboration layer that sits between systems and teams. The platform ties documents to source data, uses tagged content that can update automatically, and helps teams create, revise, comment on, and reissue documents across the product lifecycle. The point is not just faster authoring. It is preserving context, propagating change, and making downstream teams less dependent on stale text and manual search. Addy also gives concrete results from early deployments. Customers are reportedly spending about 70% less time creating documents and 95% less time updating them. She also points to gains in first-pass yield and reduced downtime in field service scenarios where technicians were previously being sent outdated PDFs that did not match the machine in front of them. This is a useful episode for anyone in PLM, manufacturing, service, quality, or industrial AI. It goes after a problem a lot of companies quietly live with every day: the digital thread looks connected on slides, but in reality, documents are still where a lot of the truth gets lost.#ThreadedMiami #Quarter20 #DigitalThread #ManufacturingAI #PLM #Documentation #FieldService #WorkInstructions #IndustrialSoftware #EngineeringOps #AI

What if the real problem in electromagnetic simulation is not user workflow, but the solver itself?In this Day 2 Threaded Miami session, Masha Petrova of Nullspace makes exactly that argument. Her claim is blunt: the core electromagnetic solvers most of industry still relies on were built decades ago for very different computing environments, and they are no longer good enough for the scale and complexity of modern RF, radar, satellite, automotive, and defense problems. Masha explains why this matters now. Electromagnetic systems are everywhere, but the hard part is not simulating an antenna in isolation. The hard part is simulating that antenna on the real platform it lives on: a satellite, a drone, a car roof, a larger system where the surrounding body fundamentally changes performance. That is where legacy tools start to break down, forcing engineers to decompose problems, approximate more aggressively, or move into physical prototyping earlier than they want. Nullspace was built to attack that gap directly. Masha positions the company as a modern full-wave 3D electromagnetic solver designed for electrically large problems, with a proprietary matrix-compression approach and multi-CPU, multi-GPU acceleration. Her pitch is not that Nullspace is a lightweight wrapper around old tools. It is that the underlying numerical engine has been rebuilt for current hardware and current problem scale. The talk gets especially strong when she grounds it in concrete examples. She walks through antenna-on-platform use cases like CubeSats and automotive shark-fin antennas, showing why the real-world body changes the electromagnetic behavior enough that isolated component simulation is not sufficient. She also highlights phased-array antenna problems, where Nullspace reportedly scales to larger cases with lower memory consumption than incumbent tools in a benchmark run by a defense customer. Another timely angle is AI readiness. Masha notes that Nullspace is built around a Python API, which makes it easier to integrate into the new generation of AI-driven engineering workflows. Her point is practical: if AI tools are going to help automate simulation setup and execution, they need tools underneath them that already speak the language of modern automation. This is a useful episode for anyone in RF, electromagnetics, aerospace, automotive, defense, or engineering software. It is not a generic “AI for simulation” story. It is a direct challenge to the assumption that the old solver layer is good enough.#ThreadedMiami #Nullspace #Electromagnetics #Simulation #RF #Radar #Aerospace #DefenseTech #EngineeringSoftware #CAE #AI

What if the real problem with engineering simulation is not physics, but the fact that the tools are still too complex, too fragmented, and too dependent on a handful of experts?In this Day 1 Threaded Miami session, John Zinn of CognaSIM lays out that case clearly. He argues that simulation teams have become a bottleneck inside engineering organizations because the software is hard to use, hard to standardize, and hard to review across teams. Companies want to democratize simulation and move it earlier into design, but in practice they are still stuck with siloed experts, repetitive setup work, and inconsistent workflows across Ansys, Altair, Siemens, and other toolchains. CognaSIM’s answer is a universal agentic AI layer for simulation. John describes a system that sits across different simulation tools and gives engineers a common workflow and interface, while also turning simulation instructions into executable plans. Instead of manually hunting through menus, searching YouTube for features, and rebuilding the same setup over and over, engineers can describe the simulation they want, let the system generate a plan, review it, and then have the tool build the simulation automatically. A big part of the talk is about wasted engineering time. John points out that teams often spend hours not on analysis itself, but on setup, repetition, and tool friction. His case study with an electrified off-road vehicle startup makes that concrete: a battery-pack analysis that took 91 minutes by hand was dramatically compressed by CognaSIM, with one especially painful task, creating bolted connections, reduced from about 20 minutes to less than a minute. The larger point is that much of simulation work is still expensive human labor spent on low-value setup rather than actual engineering judgment. He also pushes toward a more ambitious vision: simulation-driven design. Instead of running isolated analyses and handing off static results, John wants AI to help connect structural, thermal, modal, crash, and other simulations into a loop that can drive design changes, rerun workflows, and help engineers evaluate tradeoffs much earlier in development. That is a much bigger claim than “AI assistant for FEA.” It is a claim about making simulation more central, more scalable, and more reusable across the product lifecycle. Another important angle is governance and consistency. John notes that many companies already have simulation design guides and standards, but enforcing them is slow and manual. CognaSIM aims to encode those rules into workflows so teams can not only move faster but also stay aligned with required methods and review expectations. This is a strong episode for anyone working in CAE, FEA, engineering design, simulation automation, or industrial AI. It is focused less on AI hype and more on a very real bottleneck: too much valuable engineering time is still being burned just getting simulations set up and understood.#ThreadedMiami #CognaSIM #Simulation #CAE #FEA #EngineeringAI #Ansys #ProductDevelopment #DigitalEngineering #SimulationDrivenDesign #AI

In this Day 2 Threaded Miami session, Rut Lineswala of BQP makes that case head-on. He argues that the weakest link in the digital thread is often the simulation layer itself, where engineers still face huge compute demands, long runtimes, and practical limits that force them to make design decisions with extra safety factors instead of better data. His point is simple: if modeling and simulation remain too slow and too expensive, the rest of the digital thread can only be so good. Rut brings serious technical credibility to the argument. Drawing on his background in aerospace and high-performance simulation, he describes working on hypersonics-scale workflows that consumed around 200,000 CPU/GPU cores and still took close to a month to return results. That experience led him and his co-founder to a sharper question: instead of just throwing more hardware at the problem, why not redesign the solver architecture itself for the compute platforms we actually have now — and for the ones that are coming next? That is where BQP’s story gets interesting. Rut argues that many incumbent solvers were built over decades for CPU-heavy environments and have only been awkwardly ported to GPUs, which is why organizations often end up using only a fraction of the GPU capacity they are paying for. His claim is that quantum-inspired solvers can make much better use of modern architectures today, while also preparing companies for the coming shift to actual quantum hardware. In his framing, this is not just about speedups. It is about making engineering organizations “quantum ready” before that transition becomes urgent. He also grounds the pitch in real industrial outcomes. Rut shares examples ranging from optimization work that delivered lighter aerospace designs without sacrificing structural integrity to physics-AI models compressed enough to run on edge devices for space-related applications. The broader message is that the same underlying technology can cut compute costs, improve design quality, and open up workflows that were previously too slow or too expensive to attempt. This is one of the more ambitious talks from Day 2 because it is not just pitching another engineering tool. It is arguing that the future of simulation infrastructure itself is up for grabs, and that companies that stay tied to legacy solver assumptions will eventually get boxed in.If you care about CAE, HPC, quantum computing, engineering simulation, physics AI, or the next compute inflection point in industrial software, this episode is worth your time.#ThreadedMiami #BQP #Simulation #CAE #QuantumComputing #PhysicsAI #EngineeringSoftware #HPC #DigitalThread #IndustrialTech #AI #AIAcrossTheProductLifecycle

What if the biggest bottleneck in manufacturing is not engineering itself, but all the manual work that happens after the design is done and before the business can actually move?In this Day 1 Threaded Miami session, Mayank Makwana of Reeva makes that case directly. He argues that speed is the only sustainable advantage in manufacturing, but most large companies are still slowed down by the invisible layer of manual reconciliation work sitting between PLM, ERP, CRM, Git, Jira, PIM, spreadsheets, PDFs, emails, and tribal knowledge. The systems may be technically integrated, but the real process still depends on humans chasing updates, interpreting edge cases, and carrying business logic in their heads. Mayank’s point is that this hidden layer is where launches slip, costs rise, and quality breaks. A change may be approved in PLM, but weeks later the part still has not launched because somebody still has to update a spreadsheet, resolve a field mismatch, or translate engineering intent into something another system can act on. He gives examples that will sound painfully familiar to anyone in discrete manufacturing: part data that only partially maps between systems, firmware changes that trigger manual back-and-forth, and longtime employees whose undocumented judgment quietly keeps operations running until they leave. Reeva’s pitch is to replace that layer with AI agents. Instead of ripping out core systems, the platform sits between them, reads technical documents and structured data, understands what changed, identifies what downstream systems and workflows are affected, and drafts or executes the required updates. The model is designed to be reviewable, governed, and adaptive, with the system learning from exceptions over time rather than relying on brittle static scripts. That makes this a useful talk because it goes after a very real category of operational drag. A lot of industrial AI still gets framed around copilots or analytics. Reeva is going after the repetitive coordination work that actually slows launches: the emails, the spreadsheets, the undocumented rules, the schema mismatches, and the “Sarah has been here 22 years and knows how this plant works” problem. This is a conversation about turning manufacturing workflows from fragile human glue into structured, executable, reviewable automation. If you care about PLM, ERP, engineering change, product launches, or AI agents that do more than summarize documents, this episode is worth hearing.#ThreadedMiami #Reeva #ManufacturingAI #PLM #ERP #EngineeringChange #DiscreteManufacturing #AIAgents #DigitalThread #WorkflowAutomation #AI #AIAcrossTheProductLifecycle

What if factory data worked less like a pile of dashboards and more like Instagram or TikTok — surfacing the right operational insight at the right moment without forcing people to hunt for it?In this Day 1 Threaded Miami session, Jeff Tao of TDengine makes exactly that argument. He explains why industrial teams are still drowning in dashboards, alerts, and raw time-series data, while the real need is much simpler: operators, engineers, and executives want to know what matters now, why it matters, and what action to take. His thesis is that industrial software needs to move from query to feed, from pull to push, and from raw data to contextualized insight. Jeff frames the problem through the lens of his own journey as a serial entrepreneur and then goes straight into the operational pain point. Traditional factory and utility data systems still rely heavily on humans building dashboards, configuring rules, and interpreting endless streams of signals. That model is slow, brittle, and hard to scale, especially for smaller companies that cannot afford full-time data analysts or process specialists. His vision is an AI-native industrial data foundation that can detect anomalies, identify patterns, forecast outcomes, and present what is happening as a stream of meaningful operational stories rather than static charts. A major theme of the talk is contextualization. Jeff argues that raw sensor data is rarely useful on its own. What matters is turning continuous machine signals into business-relevant events with meaning: what happened, when it started, how long it lasted, how it compares to baseline, and what the likely business impact is. That is where he sees the future of time-series infrastructure going, especially in the AI era, where events and context need to become first-class citizens instead of afterthoughts layered on top of storage. He also outlines the technical stack required to make that vision real: time-series storage, real-time analytics, process analytics for root-cause work, standardized data models, asset and event modeling, contextual semantics, and AI-friendly interfaces that can expose the system to agents and other applications. The pitch is not just “let AI do it.” It is that AI only becomes useful once the underlying industrial data foundation is organized, standardized, and open enough to support meaningful reasoning. One of the sharper points in the session is who this benefits most. Jeff argues that AI-native data infrastructure can flatten access to insight for smaller and midsize industrial businesses that historically lacked the people and budget to build sophisticated analytics teams. In that sense, the talk is about more than data architecture. It is about democratizing operational intelligence. This is a useful episode for anyone working in industrial software, manufacturing analytics, time-series data, plant operations, or AI for the factory floor. It is opinionated, practical, and built around a very clear idea: the future of industrial data is not more dashboards. It is better understanding delivered automatically.#ThreadedMiami #TDengine #IndustrialAI #ManufacturingAnalytics #TimeSeriesData #FactoryData #Industry40 #OperationalIntelligence #DigitalManufacturing #AIforIndustry #AI #AIAcrossTheProductLifecycle

What if the real problem in supply chains is not visibility at the top, but the broken collaboration happening lower down the stack where the real work still runs on emails, spreadsheets, and mismatched systems?In this Day 1 Threaded Miami session, Scott and Dan Linonello of Omnae deliver one of the more memorable presentations of the event: a father-and-son talk built on decades of firsthand supply-chain pain. Their pitch is sharp. Omnae is not just another supplier portal for large enterprises. It is a natively multiplayer collaboration system built to actually work for the small and midsize suppliers who still carry a huge share of real operational complexity. Scott explains the core problem clearly: even when big companies have EDI, ERP, and supplier collaboration platforms in place, most of the actual execution still happens outside those systems. Small suppliers use different tools, different workflows, and often no real shared system at all. That creates constant mismatches in orders, invoices, revisions, quality issues, and delivery expectations. Omnae’s answer is to create a shared operational layer where enterprises get a digital twin of their supplier activity, while smaller suppliers get an actual usable application rather than a portal they are forced to feed. The talk gets more interesting when Dan takes the conversation into AI and agentic systems. His argument is blunt: most agentic tooling today lives safely in sales and reporting workflows because those domains can tolerate mistakes. Supply chains cannot. If an agent creates the wrong PO, approves the wrong operational change, or triggers the wrong action upstream, the damage is immediate and expensive. That is why Omnae treats agents as participants that can propose actions, but not execute them unilaterally. In their model, operational, legal, and financial commitments still require acceptance on the other side before a state change occurs. That makes this more than a software demo. It becomes a strong point of view on how AI should be used in operational supply chains: not as unchecked automation theater, but as a structured system for surfacing signals, proposing decisions, and escalating intelligently when confidence is not enough. Dan also explains how Omnae can pull signal from emails, WhatsApp, Slack, and direct system-to-system communication, turning messy unstructured supplier communication into something companies can actually analyze and act on. The origin story gives the whole session weight. Omnae was built from the Linonellos’ own manufacturing and sourcing experience, then hardened inside their own business before being turned outward as a product. One story in particular lands hard: a Boeing supply-chain issue that left 18 Dreamliners stuck on the tarmac over a tiny part and a revision mismatch. That experience helped shape Omnae’s multi-tier communication and permission model, designed to surface issues earlier and route them to the right level before they become major operational failures. This is a useful episode for anyone working in supply chain tech, manufacturing operations, procurement, industrial AI, or digital thread strategy. It is practical, skeptical of hype, and grounded in the ugly reality of how engineered products actually get made.#ThreadedMiami #SupplyChain #Procurement #Manufacturing #IndustrialAI #DigitalThread #SupplierCollaboration #Omnae #ERP #Operations #AI #AIAcrossTheProductLifecycle

What if one of the fastest ways to build a breakthrough engineering product is to stop reinventing infrastructure and start building on the right components?In this Day 1 Threaded Miami session, Jonathan Girroir of Tech Soft 3D gives a practical overview of the SDK and component technology landscape behind modern CAD, PLM, PDM, CAE, AEC, CAM, and digital twin applications. The core message is clear: too many startups waste time rebuilding things that already exist, when the smarter move is to focus on the differentiated layer that actually creates value. Jonathan walks through the four major pillars that sit underneath most engineering applications: data access, modeling and simulation, visualization, and the surrounding platform ecosystem. He maps out the fragmented world of open-source tools, commercial kernels, viewer frameworks, and game-engine-based platforms, explaining where products like OpenCascade, Three.js, VTK, Siemens Parasolid, Spatial ACIS, Autodesk OEM tools, and Tech Soft 3D’s own HOOPS stack fit into the picture. One of the most useful parts of the talk is that it is not vendor fluff. Jonathan is explicit about tradeoffs. Open source can be great for proof of concept work, but often breaks down when you need richer data access, stronger support, or production-grade scale. Commercial platforms bring maturity and engineering support, but also licensing complexity, ecosystem lock-in, and architectural constraints. His advice is pragmatic: align with the ecosystem your customers actually live in, and do not waste precious startup time rebuilding solved problems. He also makes the conversation timely by connecting SDK strategy to AI. Using a real CAD-data example, Jonathan shows why engineering AI starts with structured access to the data itself: topology, features, labels, part classes, and the surrounding business context. From there, he outlines how that data can be used for part classification, feature detection, similarity matching, and downstream manufacturing or sourcing decisions. The important point is that none of that works unless the data backbone is solid first. Another strong theme is the rise of gaming and digital twin platforms. Jonathan highlights how engines and platforms from the gaming world, especially around digital twins and Omniverse-style environments, are starting to reshape what is possible in industrial software. But again, his message is disciplined: flashy graphics mean little if you cannot get the engineering data in cleanly and usefully. This is a useful episode for startup founders, product leaders, and engineers building in industrial software. It is a grounded look at the hidden infrastructure layer that determines whether you get to market quickly or disappear into years of technical rework.#ThreadedMiami #TechSoft3D #CAD #PLM #CAE #AEC #DigitalTwin #EngineeringSoftware #IndustrialAI #StartupStrategy #AI #AIAcrossTheProductLifecycle

Why are so many AI-for-engineering projects still failing, even after all the hype?In this Day 1 Threaded Miami session, Andy Fine gives a sharp overview of the simulation landscape, the mission behind Fine Physics Consortium, and the uncomfortable reality that most engineering organizations still do not have the data, process, or domain grounding to make AI work in physics-heavy workflows. His core argument is simple: faster solvers, smarter data strategy, and real engineering understanding matter far more than generic AI buzzwords. Andy explains why he launched the consortium in the first place. Rather than offering another generic go-to-market pitch to startups, he wanted to curate a stack of next-generation technologies that are either dramatically faster than incumbents or capable of doing things legacy tools simply cannot do. His bar is high: if it is direct physics, it should be around 100 times faster than traditional tools, and if it is not, it should still bring a genuinely new capability while fitting cleanly into the wider engineering stack. A big theme of the talk is how stale much of the incumbent simulation market still is. Andy points out that many flagship CFD and electromagnetics solvers are decades old, built for older computing paradigms, and now facing a wave of challengers enabled by modern cloud and software development tooling. But he is equally clear that newer does not automatically mean better. He spends a lot of time on why so many AI initiatives fail in engineering, citing the gap between flashy deep-learning claims and the much harder reality of usable data, proper data processing, and domain-specific understanding. One of the most useful parts of the session is his risk framework for AI in engineering. Andy argues that before companies rush into deep learning, they should first ask five harder questions: is the application truly valuable, do they have enough data, is that data high quality, is it uniform enough to be useful, and could simpler machine learning or reduced-order modeling solve the problem more efficiently? His point is that most AI failures are not really AI failures. They are data-governance and engineering-context failures. He also makes a practical case for the future of simulation infrastructure. Instead of assuming that every next-generation engineering workflow requires massive data-center scale compute, Andy argues that newer GPU-native solvers are changing that equation, especially in domains where closed environments, security constraints, or hardware limits make giant server farms unrealistic. This episode is a useful reset for anyone working in simulation, CAE, engineering AI, or industrial software. It separates real progress from theater and explains where the next wave of value in engineering physics is more likely to come from.#ThreadedMiami #Simulation #CAE #EngineeringAI #PhysicsAI #DigitalEngineering #FinePhysicsConsortium #IndustrialAI #GPUComputing #EngineeringSoftware #AI #AIAcrossTheProductLifecycle

What actually kills most engineering software startups is not the tech.In this Day 1 Threaded Miami session, Ralph Verrilli of Next Stage Ventures delivers a blunt, highly practical talk on what it really takes to build, fund, scale, and eventually sell an engineering software company. Drawing on his background as a mechanical engineer, startup founder, operator, investor, and M&A advisor, Ralph breaks down the messy reality behind startup value creation: great technology is not enough, and most companies never make it past the early revenue ceiling because they do not build the operational discipline required to scale. What makes this conversation even better is the running commentary from Aras founder Peter Schroer, who adds hard-earned perspective from building Aras over decades. Peter is direct about the gap he now sees again and again: brilliant technical founders who can build product but have no idea how to think about sales, finance, fundraising, governance, or long-term company value. His point is sharp: too many engineers are still solving only the code problem when they should also be solving the company-building problem. The discussion goes deep into acquisition readiness, fundraising realities, cap table mistakes, diligence nightmares, and why “growth at all costs” is often just another way to build chaos. Ralph explains how weak financial controls, sloppy operating habits, one-off deals, tax issues, and bad partnership structures can all destroy value when investors or acquirers finally dig into the business. Peter reinforces the same idea from experience: if you want optionality later, you need to run the company clean from day one. There is also a useful reality check on investors. The talk contrasts angels, VCs, family offices, and private equity, making clear that each type of capital comes with very different expectations, incentives, and consequences for founders. Peter is especially candid about the downside: being fired from your own company is not theoretical. It happens. And if founders do not understand the motivations of the people on the other side of the table, they can lose control faster than they expect. This is not a generic startup pep talk. It is a field manual for technical founders in engineering, manufacturing, CAD, PLM, and industrial AI who want to avoid preventable mistakes and build something that is actually fundable, scalable, and acquirable.#ThreadedMiami #EngineeringSoftware #Startups #IndustrialAI #PLM #CAD #Fundraising #MergersAndAcquisitions #VentureCapital #ThreadMoat #AI #AIAcrossTheProductLifecycle

What does Aras actually stand for in a PLM market still dominated by legacy architecture, painful upgrades, and disconnected data?In this episode, Josh Epstein, Rob McAveney, and John Sperling lay out the Aras view of the market, the product strategy behind Innovator and Innovator Edge, and why they believe the future of PLM is not just better data management, but a genuinely operational digital thread. This is less a product pitch than a strategic overview of how Aras sees the weakness of the traditional big-three model and where it thinks the industry is headed next. Josh frames the company at a high level: Aras competes directly with Siemens, Dassault Systèmes, and PTC, but with a very different philosophy. Instead of treating PLM as a stack of stitched-together applications, he positions Aras as a digital thread platform built to be more flexible, more adaptable, and better suited to cloud and AI-era requirements. He also makes the case that the current generation of legacy PLM systems leaves most companies stuck with disconnected processes, brittle customization, and upgrade cycles so painful they become organizational events. Rob then goes deeper into the architectural argument. His view of the digital thread is broader than the traditional mechanical-CAD-centric definition: it has to span multiple disciplines, the full product lifecycle from concept through service and retirement, and the wider value chain of suppliers, partners, and customers. That is where Aras believes it differentiates. He also explains how Innovator Edge is meant to extend the digital thread beyond classic PLM screens into mobile apps, field workflows, connected services, and AI-driven use cases. On AI, the message is disciplined rather than flashy. Rob describes three layers of value: discovering data already attached to the digital thread, enriching that thread with new structured context, and amplifying its impact through better decisions and faster innovation. The important point is governance. For Aras, trustworthy AI in engineering depends on governed access to high-quality lifecycle data, along with traceability and auditability when decisions need to be explained later. John brings in the ecosystem angle. He explains how Aras works with partners across CAD integration, enterprise integrations, industry solutions, add-ons, and marketplace offerings, and why Innovator Edge opens new opportunities for outside developers to build task-based apps, connectors, and AI agents around the platform. That makes this conversation relevant not just for manufacturers evaluating PLM, but also for startups and partners looking for a route into the broader Aras ecosystem. This is a useful episode for anyone trying to understand Aras not just as a vendor, but as a platform strategy in a market that is being reshaped by AI, openness, and pressure for faster digital execution.#Aras #PLM #DigitalThread #EngineeringAI #ProductLifecycleManagement #ManufacturingSoftware #IndustrialAI #InnovatorEdge #EnterpriseSoftware #EngineeringData #AI #AIAcrossTheProductLifecycle

What happens when someone stops waiting for the engineering software world to create the right startup event and just builds it?In this episode, Michael Finocchiaro lays out the thinking behind Threaded, a conference series designed to give engineering and manufacturing startups something the market has been missing: a focused environment where founders can get in front of customers, investors, strategic buyers, and ecosystem partners without getting lost inside giant trade shows or overly narrow technical events. Michael also uses the opening keynote to frame the bigger market shift behind ThreadMoat. His argument is blunt: the engineering software startup ecosystem is much larger, better funded, and more strategically important than most people realize. He points to roughly 600 startups across design, manufacturing, simulation, supply chain, BIM, and related categories, backed by an estimated $15.7 billion in venture funding and representing tens of billions in aggregate value. In his view, this is not a niche sideshow. It is a global movement that legacy industry players, corporate buyers, and investors can no longer afford to ignore. A central theme of the talk is timing. Michael argues that the traditional PLM and engineering software giants are vulnerable, that AI is compressing workflows rather than just upgrading tools, and that companies that fail to adopt the new generation of AI-native and startup-led solutions will fall behind quickly. He also highlights the broader convergence of design, simulation, and manufacturing, suggesting that the digital thread is finally becoming operational instead of theoretical. That is what makes this more than a conference welcome speech. It is a market thesis. ThreadMoat is about mapping the ecosystem. Threaded is about activating it. And this episode captures Michael’s vision for connecting founders, customers, analysts, acquirers, and operators around one of the fastest-moving shifts in industrial technology. If you care about engineering software startups, industrial AI, digital thread strategy, PLM disruption, or where the next wave of value creation is happening in manufacturing tech, this is a sharp overview of the landscape and why it matters now.#ThreadMoat #ThreadedConference #EngineeringSoftware #IndustrialAI #PLM #DigitalThread #ManufacturingTech #Startups #VentureCapital #Innovation #AI #AIAcrossTheProductLifecycle

What if requirements engineering did not feel like 1994?In this episode, Janis Vavere and Matt Maclaine explain why Trace.Space is going straight at one of the most stubborn bottlenecks in engineering: requirements, traceability, compliance, and the painful workflows that still live in the shadow of legacy tools like IBM DOORS. Their pitch is not just that the old tools are clunky. It is that modern engineering teams need a completely different architecture if they want AI to be genuinely useful. Janis lays out the core idea behind Trace.Space: an AI-native requirements platform built from the ground up for modern systems engineering. Instead of bolting AI onto an old stack, Trace.Space is designed around a graph-based structure that makes requirements, traces, tests, and reviews machine-readable and ready for agentic workflows. The company also positions itself carefully for enterprise reality: on-prem or cloud deployment, bring-your-own-model support, and the ability to connect into a company’s own AI environment rather than forcing data into a generic public chatbot workflow. Matt then shows what that looks like in practice. Their new Space Agent can surface semantically relevant requirements, suggest traces, help build compliance and audit analyses, and generate structured outputs from the data already inside the system. The point is not just speed. It is reducing the amount of time systems engineers waste digging through pages of documentation and clicking through bloated workflows just to find what matters. A big part of the appeal here is that the AI is not a black box floating above the tool. Because it is native to the platform, users can still inspect outputs through more traditional views like traceability matrices, saved filters, reviews, and test management flows. That makes the pitch much more credible for teams working in regulated industries like automotive, aerospace, defense, and robotics, where trust and auditability matter as much as automation. This is a conversation about replacing bloated requirements management with something faster, more explainable, and more aligned with how engineering teams will actually work in the AI era.#RequirementsEngineering #Traceability #SystemsEngineering #AI #EngineeringSoftware #Compliance #ProductDevelopment #TraceSpace #DigitalThread #PLM #AI #AIAcrossTheProductLifecycle

What if the biggest problem in product development is not CAD, not PLM, and not even ERP — but the messy handoff between them?In this episode, Oleg Shilovitsky of OpenBOM goes straight at one of the least glamorous but most expensive problems in manufacturing: engineering creates the product, then execution starts with spreadsheets, PDFs, disconnected files, and tribal knowledge scattered across departments. Procurement gets the wrong unit of measure, maintenance loses the original reasoning behind a change, and costly mistakes hit the business immediately. Oleg explains how OpenBOM was built around that disconnect. His argument is simple: most companies still run on files, and despite all the cloud talk, a huge share of CAD data is still trapped in desktop environments like SOLIDWORKS. That means the real challenge is not forcing companies into a giant new platform, but giving them a simpler way to organize product data, preserve context, and move information cleanly across engineering, procurement, suppliers, and operations. The new angle is AI. Oleg introduces OpenBOM’s new file agent for SOLIDWORKS, designed to work inside the reality companies already have instead of demanding a painful rip-and-replace transformation. The agent is built to capture revisions, manage file-based workflows, and collect the context that usually gets lost in emails, memory, or half-documented records. He frames this as the start of a broader “product memory” strategy: capture information, review it intelligently, and then flow it to the people and systems that need it. He also lays out what comes next: review agents that analyze BOMs before release, catch errors, and help route clean information into ERP systems, contractors, and suppliers. That sounds basic until you remember how much engineering time and procurement time gets burned every day cleaning up preventable mistakes. This is a conversation about a very unfashionable problem with very real dollar impact. If you care about BOMs, file-based CAD, engineering handoff, procurement errors, AI agents in PLM, or the gap between design and execution, this one is worth hearing.#OpenBOM #PLM #BOM #SolidWorks #EngineeringData #Procurement #Manufacturing #IndustrialAI #DigitalThread #ProductDevelopment #AI #AIAcrossTheProductLifecycle

What if the real bottleneck in hardware development is not design itself, but the chaos of explaining design changes to everyone else?In this episode, Pradyut Paul of Bild breaks down a problem every engineering organization knows too well: design changes happen fast, but the reasoning behind those changes usually stays trapped inside the engineer’s head. That slows down ECO reviews, drags teams into endless meetings, and turns engineers into human translators for manufacturing, supply chain, finance, and leadership. Pradyut explains how Bild is attacking that problem with Meru, its new AI agent for design change management. Meru is built to analyze CAD revisions, detect what changed, and then go a step further by explaining the design context and likely why the change happened. The goal is simple but important: make engineering change orders easier to understand, faster to review, and far less dependent on manual screenshots, annotations, and repetitive meetings. The discussion gets into what makes this interesting technically. Meru is not just looking at geometry in isolation. It combines feature recognition, revision history, commit data, and geometry relationships to build a more complete picture of change at both the part and system level. That is the difference between a basic diff tool and something that starts to behave more like an engineering reasoning layer. Pradyut also shares early customer results. According to Bild, early deployments are showing a 60% reduction in change-order cycle time, with teams saving roughly two to three meetings per change order. For hardware teams trying to move faster without burying engineers in administrative overhead, that is a serious claim. This is a conversation about where AI becomes genuinely useful in engineering: not writing fluff, but compressing decision cycles, surfacing design intent, and helping products get out the door faster.#EngineeringAI #PLM #PDM #ECO #ChangeManagement #CAD #HardwareDevelopment #DigitalThread #Manufacturing #Bild #AI #AIAcrossTheProductLifecycle

Jonathan Girroir explains why Tech Soft 3D’s SpinFire product line matters far beyond “just viewing CAD.”This conversation gets into one of the messiest problems in engineering: how to move complex product data across the enterprise without forcing everyone onto a full CAD seat or trapping information inside a single system. Jonathan breaks down how SpinFire tackles that problem through three core areas: native CAD translation, rich 3D PDF publishing, and lightweight enterprise visualization, with XR also entering the mix for design review and factory use cases. A big part of the discussion is practical interoperability. Jonathan walks through how SpinFire Convert can move engineering data from one ecosystem to another, including common workflows like 3DEXPERIENCE to JT, while preserving assembly structure, geometry, attributes, PMI, and other rich engineering data. He also shows how that same data can be turned into far more useful technical packages instead of static documents. Another key theme is access. SpinFire Insights is positioned as a lightweight, enterprise-friendly way to get 3D data into the hands of more people across the business, from engineering to procurement, tooling, and manufacturing. The point is simple: product data is too valuable to stay locked inside CAD and PLM silos. Jonathan also touches on the broader Tech Soft 3D strategy, including how acquisitions like Actify and Theorem helped shape the SpinFire portfolio, and how the line between toolkits and end-user applications is starting to blur as more engineering workflows move into the cloud. If you care about CAD interoperability, engineering data reuse, 3D PDF workflows, XR for industry, or getting product data to more people without more software overhead, this episode is worth your time.#CAD #PLM #TechSoft3D #SpinFire #EngineeringData #Interoperability #3DPDF #XR #DigitalThread #Manufacturing #AI #AIAcrossTheProductLifecycle

Garth Coleman, CEO of Canvas Envision breaks down one of the biggest blind spots in the digital thread: most companies connect systems, but they still do not truly connect the frontline worker.In this conversation, he explains how Envision is using AI, 3D, video, and visual work instructions to turn engineering intent into something operators can actually use on the shop floor. The discussion covers the shift from static manuals and PDFs to interactive execution, why legacy documentation is too slow and too disconnected, and how AI can cut work-instruction authoring from days or weeks down to minutes.A key theme is practical ROI. This is not AI for slideware. It is AI applied to reducing errors, scrap, training time, and the friction of constant engineering change. Garth also walks through how Envision connects systems of record like PLM and PDM with systems of execution, helping extend the digital thread all the way to manufacturing and service execution.If you care about connected manufacturing, frontline enablement, industrial AI, visual work instructions, or the real-world future of the digital thread, this episode gets very concrete very quickly.Topics include:AI-assisted work instruction authoringVisual execution for the frontlineConnecting PLM, PDM, and MESTurning video and 3D into interactive instructionsReducing training time, errors, and scrapMaking the digital thread operational#DigitalThread #IndustrialAI #Manufacturing #PLM #MES #ConnectedWorker #WorkInstructions #SmartManufacturing #Engineering #FrontlineOperations #AI #AIAcrossTheProductLifecycle

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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 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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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 #AI #AIAcrossTheProductLifecycle

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 #AI #AIAcrossTheProductLifecycle

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 #AI #AIAcrossTheProductLifecycle

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 #AI #AIAcrossTheProductLifecycle

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 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 #AI #AIAcrossTheProductLifecycle

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 #AI #AIAcrossTheProductLifecycle

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 #AI #AIAcrossTheProductLifecycle

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

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#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

The conversation with Ross Meyercord and Kishore Subramanian covers their journey in the AI space, the impact of AI on software development, and the implementation of AI within Propel's solution. They discuss the use of AI in coding, the introduction of Agent Force, and the specific skills and use cases within the platform. The conversation also highlights the transformative impact of AI on productivity and the user experience. The conversation covers various aspects of AI-powered solutions, their impact on digital maturity, and the challenges associated with their deployment. It also delves into the role of agents in driving business value and the importance of managing digital workers effectively.TakeawaysAI's impact on software developmentThe implementation of AI within Propel's solution AI-powered solutions drive digital maturity and have the potential to transform the way businesses operate.The management and coaching of digital workers are essential for ensuring their effectiveness and aligning them with business goals.Chapters00:00 Skills and Use Cases36:57 Managing Digital Workers and Agents43:43 Economics of AI-Powered Solutions#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

The conversation begins with an introduction and background of the companies, Quix and Dalus, followed by an overview of their respective focuses. The discussion then delves into the initial reaction to the AI revolution, the implementation of AI in product development, and the usage of AI in user interface and plumbing. The framework of Advise, Assist, and Automate is explored, along with the challenge of addressing the probabilistic nature of AI in the deterministic engineering world. The conversation delves into the future of AI in engineering, discussing prompt engineering, multi-agent processes, system architecture, and the impact of AI adoption on organizations. It also explores the role of CAD and PLM professionals and the skills required for the next generation of engineers. The importance of data maturity in engineering organizations is highlighted, emphasizing the cultural impact of AI adoption and the need for a shift in organizational culture.TakeawaysAI as an AcceleratorCustom Solutions with AI AI adoption in engineering is a culture questionThe importance of data maturity in engineering organizationsChapters00:00 Introduction and Company Backgrounds07:36 AI Implementation in Product Development15:09 AI Usage in User Interface and Plumbing23:28 Framework of Advise, Assist, and Automate28:51 Addressing Probabilistic Nature of AI in Engineering#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

The conversation delves into the evolution and adoption of AI in the manufacturing industry, covering topics such as the founding and evolution of companies, initial skepticism and perception of AI, AI in software development and agile transformation, exploration of AI tools and use cases, AI tools and product management, implementation of AI in systems and user interaction, AI autonomy and workflow specificity, complexity of manufacturing and AI adoption, deterministic vs. probabilistic AI in manufacturing, guardrails and contextual data in manufacturing AI, training and implementation of LLMs in manufacturing, and the future of AI in manufacturing. The conversation delves into the emergence of AI demand in the market, the behavior of the market based on company size, the consolidation of startups and monolithic systems, the platform approach of large incumbents, complexity and financing in technology development, the holistic approach to AI in manufacturing, customer digital maturity and AI adoption, data connectivity and digital maturity challenges, AI implementation and digital maturity acceleration, and advice for young engineers in the AI era.TakeawaysAI in ManufacturingAI Adoption in Manufacturing AI demand is emergingMarket behavior varies based on company sizeChapters00:00 Introduction and Company Overview07:24 Exploring AI Tools and Use Cases13:02 AI Autonomy and Workflow Specificity21:30 Guardrails and Contextual Data in Manufacturing AI26:47 Future of AI in Manufacturing32:13 Market Behavior Based on Company Size37:34 Holistic Approach to AI in Manufacturing45:37 Data Connectivity and Digital Maturity Challenges50:59 AI Implementation and Digital Maturity Acceleration#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

The conversation delves into the intersection of AI and 3D technology, exploring the history, collaboration, and evolution of AI in 3D product development. It also discusses the integration of AI in 3D development, data processing, and the future of AI in the 3D space. The conversation delves into the integration of AI with real-time 3D projects and the challenges of data access and integration in the context of AI implementation. It explores the impact of AI on engineering and the relevance of AI in the future of engineering work.TakeawaysAI in 3DAI and 3D DataAI in Product Development AI and 3D IntegrationChallenges of Data Access and IntegrationChapters00:00 Introduction to 3D and AI05:28 AI Integration in Development12:14 AI in 3D Product Development24:43 AI and 3D Data Processing30:48 AI and 3D Integration47:32 Challenges of Data Access and Integration#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

The conversation begins with an introduction and background of the participants, followed by a discussion on the early impressions of the AI revolution and its transformational impact. The adoption of AI in manufacturing and its impact on the development workflow are explored, along with the implementation of AI in products. The focus on process industries is also highlighted. The conversation delves into the evolution of AI, the training and implementation of AI models, the impact of AI on manufacturing, and user adoption and industry challenges. It explores the changing perspectives on AI, the role of AI in manufacturing, and the challenges faced in AI adoption within the industry.TakeawaysAI Revolution in ManufacturingImpact of AI on Development Workflow AI's impact on workforceAI's role in manufacturingChallenges in AI adoptionChapters00:00 Focus on Process Industries32:09 The Evolution of AI41:05 Training and Implementing AI Models53:13 Impact of AI on Manufacturing59:18 User Adoption and Industry Challenges

The conversation delves into the evolution and application of AI in engineering, software development, and manufacturing. It explores the use of AI as a force multiplier and the integration of deep technologies for AI in engineering. Additionally, it discusses the user interaction with AI in engineering software and the future of AI in engineering and manufacturing. The conversation delved into the complexity of CAM and assembly, highlighting the high bar of expertise required to solve complex parts in industries such as aerospace, defense, and medical. It also explored the challenges in digital maturity, with insights into the digital capabilities of manufacturing companies and the adoption of advanced technologies.TakeawaysAI as a Force MultiplierDeep Technologies for AI Integration Complexity of CAM and AssemblyChallenges in Digital MaturityChapters00:00 Introduction and Company Backgrounds06:26 AI Integration in Manufacturing and Work Instructions18:03 Deep Technologies for AI Integration in Engineering23:59 The Future of AI in Engineering and Manufacturing43:15 Challenges in Digital Maturity

The conversation delves into the application of AI in engineering, including skepticism, historical perspective, and the impact on productivity. It also explores AI implementation in product development, simulation, CAD cleanup, and design optimization. The discussion touches on GPU vs. TPU, AI integration in engineering software, and the future of engineering and AI. The conversation delves into the realities of AI in engineering, the future of work and engineering education, digital maturity in engineering, and the impact of AI on engineering workflows. It emphasizes the challenges, opportunities, and realistic approach to AI in the engineering industry, as well as the importance of fundamental knowledge in engineering education. The discussion also highlights the impact of AI on future job prospects and the adoption of advanced tools in engineering.TakeawaysAI's impact on productivityAI integration in engineering software Realistic approach to AI in engineeringImportance of fundamental knowledge in engineering educationChapters00:00 Introductions and Background08:15 AI in Development and Engineering13:38 AI in Simulation and CAD Cleanup20:11 Engineer's Perspective on AI27:27 AI Integration in Engineering Software33:20 The Future of Engineering and AI42:00 The Future of Work and Engineering Education53:33 The Impact of AI on Engineering Workflows#AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino