Its SDKs sit beneath hundreds of CAD, CAM, CAE, additive manufacturing, construction and PLM applications. Now the company is betting that the next competitive frontier will be built around something even more fundamental: access to rich, contextualized 3D engineering data.

In this special edition of AI Across the Product Lifecycle, I speak with Jonathan Girroir of Tech Soft 3D about the changing architecture of engineering software and the company’s latest moves.

We discuss why engineering workflows are shifting from files toward APIs, why cloud adoption is producing hybrid rather than purely cloud-native architectures, and why multi-CAD support now requires far more than importing neutral geometry.

Jonathan explains the thinking behind HOOPS AI, Tech Soft 3D’s framework for encoding native 3D geometry for machine-learning applications. We examine practical use cases including geometry search, feature recognition, part reuse, costing and the connection of CAD data with procurement and manufacturing information.

We also challenge some of the industry’s louder claims.

Is text-to-CAD genuinely close to transforming engineering, or is the industry underestimating the difficulty of training models on accurate, structured design data? Could OpenUSD become a common contextual layer across engineering and manufacturing? And will engineering experience its own “OpenAI moment” before 2030?

The conversation also covers Tech Soft 3D’s emerging Data Hub, QIF, DGN, cellular-volume visualization, lightweight web viewers, build-versus-buy decisions, startup support and the longer-term impact of quantum computing on simulation.

A grounded discussion about where engineering AI is delivering value today, where the hype is outrunning reality, and why the unglamorous glue between systems could remain one of the industry’s biggest opportunities.

Timeline

00:00 Introduction
00:37 What Tech Soft 3D does today
01:21 HOOPS, SpinFire and 30 years of engineering software
02:41 Cloud, hybrid architectures and data sovereignty
05:30 Connected workflows and engineering ecosystems
06:33 Why rich multi-CAD data matters
07:51 What engineering-software customers now demand
09:29 Rendering, OpenUSD and new interoperability models
10:53 Tech Soft 3D’s major 2026 announcements
11:09 Introducing HOOPS AI
12:24 Text-to-CAD: breakthrough or premature hype?
13:31 Geometry search, part reuse and contextualized 3D data
15:01 SpinFire mobile, cellular volumes and the Data Hub
16:32 The customer problems behind the roadmap
18:31 Why HOOPS AI generated the strongest response
20:15 Supporting engineering products measured in decades
22:43 What customers actually want from AI
23:26 Where AI creates real engineering value
24:04 Where engineering AI is overhyped
24:46 Why topology, PMI and design intent matter
25:49 Extending visualization across the enterprise
27:59 Web-based 3D collaboration and distributed teams
28:40 Build versus buy in engineering software
31:02 Openness, interoperability and intellectual property
33:36 Advice for engineering-software startups
35:17 How Tech Soft 3D supports early-stage companies
35:49 Will engineering have an OpenAI moment?
38:45 Quantum computing and the future of simulation
40:15 The underestimated opportunity between systems
40:37 What is next for HOOPS AI and the Data Hub
41:16 How to begin evaluating Tech Soft 3D
41:42 Closing thoughts

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with two French industrial AI founders building directly into that shift:

Matthias Berahya-Lazarus, CEO & co-founder of Cognyx
Thibaut Wilhelm, CEO & founder of Oplit

Cognyx is building an AI engineering platform for hardware — what Matthias frames as “Claude Code for industrial products.”

Oplit is building an AI supply chain platform for industrial companies — using agentic supply chains to optimize factory performance.

This conversation goes well beyond generic copilots.

We talk about why AI may expose that many companies never had a real digital thread, why supply chain is such a strong playground for agents, why engineering AI needs executable knowledge rather than another chatbot, and why the future factory will be far more software-heavy, automated, and AI-native than most people expect.

Key themes:

• AI shifting the bottleneck from execution to product thinking
• Supply chain as code
• The move from planners doing repetitive scheduling to managers defining objectives
• Why advanced factories may stop planning in Excel by 2030
• Why engineering needs composable, executable knowledge
• Why digital maturity determines whether industrial AI works or fails
• Why reindustrialization will not look like old factories coming back
• Why the most interesting industrial AI companies are solving deterministic, messy, high-value operational problems

If you care about PLM, digital thread, engineering software, manufacturing AI, MES, supply chain planning, industrial data, or the future of European reindustrialization, this is a conversation worth hearing.

#IndustrialAI #AgenticAI #SupplyChainAI #EngineeringAI #DigitalThread #PLM #ManufacturingAI #FactoryOfTheFuture #Reindustrialization #HardwareEngineering #Cognyx #Oplit #ThreadMoat

Most of it still sounds like science fiction.

But what happens when you put two founders building real AI-native engineering software in the same conversation?

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Pradyut, co-founder of Bild, and Martin Bielicki, co-founder and CEO of Bench, about what AI is actually changing in engineering software, CAD data, simulation, product development, and manufacturing workflows.

Bild is building CAD data management that connects engineering to manufacturing. Bench is building an AI orchestration layer across CAD, simulation, PLM, and beyond.

The discussion cuts through the hype:

AI is already changing how startups code. QA and validation are becoming the bottleneck. Prompting matters less than context. Frontier model cost is becoming a real burn-rate issue. And engineering AI will not move as fast as software AI because CAD, simulation, manufacturing, sourcing, and PLM are different technical worlds.

The real unlock is not “AI replacing engineers.”

Timeline

00:00 – Introduction: Bild, Bench, and AI across engineering
00:29 – Pradyut introduces Bild
00:54 – Martin introduces Bench
01:16 – The OpenAI moment
01:55 – Bench was created because of the LLM breakthrough
02:25 – Bild’s early exposure to DALL·E
03:33 – How AI changed startup coding
04:01 – Cursor, Claude Code, Slack, Graphite, and same-day delivery
05:45 – Multi-agent development
06:37 – Why “looking good” is becoming commoditized
07:28 – Why old software stacks limit AI innovation
08:21 – Prompting vs context
09:53 – From prompts to loops
10:40 – Frontier LLM costs
11:20 – Token costs as the new AWS-style shift
12:15 – AI spend caps and productivity measurement
13:45 – Cheaper models and model routing
14:39 – Right model, right task
15:39 – AI and engineering org structure
16:19 – QA, validation, and human-in-the-loop checks
17:58 – How AI may reorganize hardware teams
18:50 – Multi-agent coding conflicts
21:16 – Where AI lives inside the product stack
21:42 – Bench: AI for context, planning, and judgment
22:43 – Bild: opt-in AI for CAD data and IP boundaries
24:19 – When will engineering have its OpenAI moment?
25:04 – Why engineering AI evolves use case by use case
26:35 – Faster adoption in consumer products?
27:04 – From text-to-CAD to DFM and manufacturability
29:09 – The coming CDFAM AI demo wave
30:05 – Advice for young engineers
30:43 – Don’t compete with agents. Build differentiated skills.
33:05 – Creativity roles and AI in physical sciences
35:06 – Why top engineers become more valuable
35:57 – Digital transformation reality check
37:21 – Prints, redlines, and physical sign-offs are still alive
39:31 – Can startups move faster than legacy vendors?
40:10 – Big OEMs asking for AI engineering visions
41:15 – Buying AI vs buying value
42:50 – Why transformation programs route back to incumbents
43:37 – Build vs Windchill
45:30 – Startup visibility vs legacy vendors
47:56 – Capability checkboxes vs real user experience
49:13 – AI agents for CAD and CAE workflows
49:33 – End-to-end orchestration and organizational readiness
51:49 – Keeping skilled engineers in the loop
52:26 – Trust but verify for hardware AI
53:39 – Where to meet Bild and Bench
55:31 – Closing remarks

Featuring Pradyut of Bild and Martin Bielicki of Bench. Hosted by Michael Finocchiaro.

#AI #EngineeringSoftware #CAD #PLM #Simulation #Manufacturing #DigitalThread #IndustrialAI #HardwareEngineering #Startups

The BOM debate is back. And somehow, it got even more dangerous.

In this Future of PLM panel, Michael Finocchiaro brings together Christine Longwell, Gus Quade, Brion Carroll, Pat Hillberg, David Schultz, and Oleg Shilovitsky for a high-energy debate on one of the most persistent fractures in product lifecycle management:

Who really owns the Bill of Materials?

Engineering says the EBOM defines the product.
Manufacturing says the MBOM defines what can actually be built.
ERP says the operational BOM is what matters.
MES wants execution context.
Service wants the as-maintained truth.
And AI? AI is useless unless all of this data is normalized, contextualized, and connected.

This episode goes deep into EBOM vs MBOM, recipes vs discrete manufacturing, fashion vs aerospace, service BOMs, circular economy, Conway’s Law, ISA-95, product memory, data governance, and why every “single source of truth” eventually collides with organizational reality.

The conclusion?

The BOM is not just a list of parts.
It is a battleground between systems, silos, budgets, ownership, and the future of industrial AI.

Timeline

00:00 – Introduction: BOM Wars, Part 2
00:54 – Christine Longwell and Gus Quade join the panel
02:20 – Autodesk’s MaintainX acquisition and service implications
03:14 – Jörg Fischer’s provocation: “The BOM doesn’t exist”
04:30 – ERP BOM vs MES vs MBOM: where does manufacturing truth live?
05:31 – Engineering defines the product, manufacturing defines the action
08:04 – Why BOM logic changes by industry
09:50 – Fashion, fabric, tech packs, suppliers, and PLM
12:29 – Should EBOM, MBOM, as-built, as-shipped, and as-serviced live in one system?
14:55 – Product memory and algorithmic BOM transformation
16:00 – Conway’s Law: why BOM structures mirror organizations
19:21 – Can product memory connect engineering and manufacturing logic?
21:02 – Is the MBOM a separate object or just a different view?
22:33 – Digital thread, service BOMs, and lifecycle responsibility
24:44 – TWA 800, aircraft traceability, and why as-built data matters
27:30 – Why silos exist because organizations exist in silos
28:29 – AI orchestration across PLM, MES, ERP, and service
30:36 – Service BOM, software BOM, spare parts, and terminology chaos
32:31 – Why AI needs normalized data before it can add value
33:58 – Conway’s Law and the limits of database-driven transformation
37:55 – EBOM vs MBOM through the CAD and manufacturing lens
39:31 – When does a product become “real”?
41:47 – CIOs, data governance, and who should own cross-silo truth
44:38 – Autodesk’s data model approach and the unified product record
45:06 – PTC Orbit, Jetstream, and the race toward digital thread platforms
48:36 – Master data models, ontologies, and common exchange standards
50:32 – Closing question: what BOM belief will be wrong in five years?
51:27 – Oleg: data misalignment and unit-of-measure disasters
54:44 – Gus: CAD-optimized vs ERP-optimized structures will stop being binary
57:37 – David: the danger of returning to point-to-point integrations
59:14 – Brion: role-based UX on top of shared product ontology
61:16 – Pat: digital thread will eventually collapse EBOM/MBOM boundaries
62:50 – Christine: BOM is product definition, not just a parts list
63:57 – Do we need BOM Wars Part 3?

Featuring:
Christine Longwell
Gus Quade
Brion Carroll
Pat Hillberg
David Schultz
Oleg Shilovitsky
Hosted by Michael Finocchiaro

#PLM #BOM #EBOM #MBOM #DigitalThread #Manufacturing #ERP #MES #EngineeringSoftware #AI #ProductLifecycleManagement #FutureOfPLM

For the first time since 2008, Gartner has published a Magic Quadrant for PLM — and Aras is now positioned alongside the traditional PLM giants.

In this breaking-news episode of AI Across the Product Lifecycle, I sit down with Josh Epstein, CMO of Aras, to unpack what this means for the PLM market, why digital thread has become central to enterprise software strategy, and why “governed engineering AI” may be the real battleground for the next generation of product development platforms.

We discuss why PLM has become too important for analysts to ignore, how Aras positions itself differently from Siemens, Dassault Systèmes, and PTC, and why AI in engineering cannot just be another copilot bolted onto messy enterprise data.

The key question:
Can AI transform engineering without a governed, explainable digital thread underneath it?

Josh also goes deep on Aras Innovator Edge AI, Thread RAG, product memory, context graphs, lifecycle-aware AI agents, and what the engineer’s workday could look like when PLM starts decomposing into governed micro-experiences and agent-driven workflows.

If you care about PLM, digital thread, engineering AI, enterprise software, or the future of product development, this one matters.

⏱ Timeline
00:00 — Breaking news: Gartner brings back the PLM Magic Quadrant
00:36 — Why did Gartner wait so long after 2008?
02:06 — Has the PLM market fundamentally changed?
04:28 — Why PLM is more complex than ERP or CRM
06:05 — Aras vs. the “Big Three” PLM incumbents
06:37 — Why CAD-agnostic PLM may now be an advantage
07:21 — Governed engineering AI vs. generic AI hype
09:37 — Trust, governance, observability, and explainability
11:34 — Why AI needs the digital thread to be actionable
12:45 — PLM data complexity: versions, effectivity, access, context
15:05 — How to market AI in 2026 without overpromising
15:53 — Aras Innovator Edge AI, Thread RAG, and workflow agents
17:08 — Product memory, context graphs, and decision traces
19:08 — Does Gartner validation change the sales conversation?
21:17 — Is PLM still the right category name?
23:50 — Cognitive digital thread vs. product memory
26:00 — What does an engineer’s day look like in three years?
27:00 — Adaptive PLM, micro-experiences, and agent-driven work
29:30 — Why PLM AI cannot just be dumped into a data lake
31:30 — The physical-world constraint: “close enough” is not enough
32:00 — Has PLM had its OpenAI moment yet?

🎯 Subscribe for more conversations on AI, PLM, CAD, manufacturing software, digital thread, and the next generation of engineering platforms.

💬 Comment THREAD if you want more deep dives on PLM, governed AI, and the engineering software startups reshaping this market.

#PLM #DigitalThread #EngineeringAI #Aras #Gartner #MagicQuadrant #ProductLifecycleManagement #AI #EnterpriseAI #Manufacturing #CAD #PDM #ProductDevelopment #IndustrialAI #AgenticAI #AIEngineering #DemystifyingPLM #ThreadMoat

In this special Share PLM Summit 2026 edition of The Future of PLM Podcast, host Michael Finocchiaro is joined by Jos Voskuil, Oleg Shilovitsky, Rob Ferrone, Patrick Hillberg, Nina Dar, and Maria Morris for a candid, unscripted discussion about the ideas that emerged from one of the industry’s most unique events.  

The conversation explores why the human side of PLM remains the hardest part of transformation, whether AI will fundamentally reshape consulting and knowledge work, how organizations build trust during digital change, and why community may be becoming more important than technology itself.

From AI adoption and organizational change to conference design and the future of professional expertise, this episode offers practical insights and thought-provoking perspectives from some of the industry’s most experienced practitioners.

Topics Covered

• The evolution of Share PLM Summit and its human-centered approach
• AI’s impact on engineering, consulting, and PLM careers
• Why trust may be the real ROI of conferences
• Lessons from successful and unsuccessful PLM transformations
• Human adoption versus technical implementation
• Digital transformation beyond software deployment
• The future of work in an AI-driven world
• Community, collaboration, and knowledge sharing

Timeline

00:00 Welcome & introductions
01:20 Why Share PLM Summit feels different
03:30 Breaking away from traditional PLM conferences
05:45 Why attendees travel across continents to attend
07:35 PLM as a people-centered discipline
09:50 AI, digital overload, and human connection
12:40 Measuring conference ROI beyond leads and sales
15:10 Most impactful presentations from the summit
20:05 Data, AI, and the Gentelligence perspective
22:10 Helena Haapio’s keynote and the future of work
24:50 Will AI replace consulting and expertise?
30:05 AI, critical thinking, and engineering risk
31:10 Sponsors, trust, and community building
36:00 Workshops, learning, and audience engagement
42:20 Sustainability and digital product passports
48:20 The Share Nest initiative
51:55 The future of conferences and professional development
58:20 Trust as the new business currency
01:01:00 Community, networking, and collaboration
01:03:40 The value of disagreement and debate
01:05:00 One word that defines Share PLM Summit 2026
01:07:00 Closing thoughts

#PLM #AI #DigitalTransformation #Engineering #Manufacturing #Industry40 #DigitalThread #DigitalTwin #ProductLifecycleManagement #IndustrialAI #FutureOfPLM #SharePLM #EngineeringLeadership #SystemsEngineering #Innovation #TechnologyLeadership

It fails when the answer cannot be trusted.

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Fay Goldstein, Co-Founder and CEO of Bardin AI, and Scott Lionello, Co-Founder and CPO of Omnae Technologies, about where industrial AI is really going: beyond chatbots, beyond copilots, and into the operational workflows that actually run manufacturing businesses.

Bardin AI is building an application engineer for industrial automation sales and support teams, helping them answer complex technical questions without escalating everything to senior engineers. Omnae is building supply chain collaboration software that allows AI agents to operate safely across real suppliers, buyers, orders, invoices, and messy enterprise data.  

The conversation goes straight into the hard parts of industrial AI:

trust, auditability, determinism, human-in-the-loop workflows, knowledge graphs, API costs, token burn, procurement risk, sales engineering bottlenecks, and why “just add a chatbot” is not enough when mistakes touch contracts, general ledgers, supply commitments, or customer trust.

Fay and Scott also discuss how AI is changing startup operations and software development, why young professionals need to show AI fluency rather than fear AI replacement, and why mid-market manufacturers may adopt practical AI faster than large enterprises waiting for top-down transformation programs.

The big takeaway: the next wave of industrial AI will not be about flashy demos. It will be about operational relief.

Fewer escalations.
Faster quoting.
Cleaner supplier collaboration.
Better support workflows.
Safer automation.
More trust in the decisions AI helps make.

This is a grounded, founder-level conversation about how AI is moving into the less glamorous but highly valuable parts of the product lifecycle: sales, support, procurement, supply chain, and the industrial back office.

Topics covered: industrial AI, agentic AI, supply chain AI, procurement, pre-sales engineering, industrial automation, knowledge graphs, AI trust, human-in-the-loop workflows, manufacturing software, digital transformation, enterprise AI, startup innovation, and the future of AI across the product lifecycle.

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Chloë Guidi of Qonic and Moritz Rietschel of Raven about the AI-native future of CAD, BIM, and AEC workflows.

Qonic is building a modern, cloud-based BIM platform from scratch, including its own solid modeling kernel, with a mission to make BIM lighter, faster, more accessible, and more data-rich. Raven is building AI-first workflows for complex design environments like Rhino, Grasshopper, Revit, Tekla, and Archicad, helping users navigate fragmented toolchains with less friction.

The conversation cuts through the hype and focuses on what is actually changing:

AI-assisted software development.
AI-native design workflows.
Smarter BIM quality checks.
More accessible CAD and AEC tools.
The economics of LLM-powered software.
The difference between “software built with AI” and “software that only makes sense because AI exists.”

Chloë and Moritz also discuss whether engineering and BIM are heading toward their own “OpenAI moment,” why open standards and data quality matter, and what young engineers should do as AI changes the skills required to stay relevant.

This is a practical, founder-level look at how AI is moving into the real workflows of design, modeling, validation, and engineering decision-making.

Topics covered: AI in CAD, AI in BIM, AEC software, digital twins, Rhino, Grasshopper, Revit, engineering workflows, AI coding, MCP, open standards, startup innovation, and the future of AI-native engineering tools.

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, Campfire
Oluwaseyi “Shay” Sosanya, Co-Founder & CEO, Gravity Sketch
Host: Michael Finocchiaro, AI Across the Product Lifecycle

Transcript source:  

Timeline

00:00 Welcome and guest introductions
03:05 Jay Wright on being bullish about AI after ChatGPT
04:33 Shay Sosanya on cautious optimism and the speed of AI progress
07:06 Why 3D geometry is harder for AI than language
08:42 AI capabilities are moving faster than expected
10:07 How Gravity Sketch adopted AI in software development
12:27 Campfire’s AI-assisted development workflow
13:32 AI agents in meetings, code, and product workflows
16:11 Using AI with existing 3D assets, BOMs, documents, and legacy data
18:26 Campfire’s spatial workflows for engineering, training, and sales
20:02 Where AI sits in the software stack
20:28 Campfire’s spatial agent as a first-class user
21:46 Gravity Sketch’s human-first approach to AI in spatial design
23:36 Foundation models, 3D generation, and geometry engines
25:29 AI cost, IP protection, customer data, and bring-your-own-LLM models
28:00 Has engineering had its ChatGPT moment yet?
29:05 Why physical product development will see staged AI adoption
31:17 The engineering-to-manufacturing gap
32:13 Simulating manufacturing workflows before production
34:12 AI connectors, Blender, Fusion 360, and tool control
35:18 Advice for young engineers worried about AI
39:41 Making real products, not just AI-generated concepts
40:00 Digital maturity in industrial companies
41:21 Why many manufacturers remain at low digital maturity
42:31 Headsets, cloud, InfoSec, and adoption barriers
43:39 Employees are already using AI and immersive tools informally
46:57 Can agile startups move industrial customers faster than incumbents?
48:17 Campfire on solving workflows rather than selling AI novelty
50:29 Gravity Sketch on value, workflow depth, and avoiding AI hype
53:09 Where to see Campfire and Gravity Sketch next
56:12 Closing thoughts

Riverside Event Description
Everyone 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?

Timeline
00:16 — Introduction: single source of truth, broken digital threads, and Product Memory
03:02 — Oleg defines Product Memory beyond single source of truth and digital thread
06:28 — Rob on dependency graphs and hidden context in unstructured documents
08:36 — Brion on Product Memory as an “orb” fed by siloed enterprise systems
11:39 — Jonathan on semantics: why “part” means different things across functions
13:46 — David on Product Memory from an enterprise architecture perspective
18:21 — Avoiding inconsistent data across PLM, ERP, PIM, e-commerce, and supply chain
22:09 — Why engineering-to-manufacturing translation is so hard
25:00 — Why engineering release is not the finish line
30:05 — Missing memory: decisions in people’s heads, spreadsheets, and informal actions
33:57 — Why skipping change steps can slow the enterprise down
35:57 — AI agents, requirements ingestion, and asking “why” like a three-year-old
39:48 — Why AI agents must document their own reasoning
42:49 — Product Memory flywheel: capture, review, flow, and distribution
45:35 — Industrial AI, physical AI, agentic AI, and real-time product memory
48:21 — Semantic consistency, meta layers, and vetting data before Product Memory
52:15 — Dependency graphs, imperfect data, and improving ontology over time
55:12 — Human maturity: is the organization ready?
56:56 — Where companies should start looking for missing Product Memory
1:03:58 — Rob’s call to action: start capturing decision traces now
1:05:03 — Closing: eBOM, mBOM, ISA-95, and semantic translation

This 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 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.

Timeline
00:00 — Introduction: Vinci, Fine Physics Consortium, and the “OpenAI moment” for simulation
01:11 — What is physics intelligence?
02:18 — Why physics is universal and governed by differential equations
03:08 — Physics-based AI vs. surrogate models
04:01 — What makes a physics foundation model credible?
06:51 — Why business value beats white papers
08:33 — Where Vinci fits in the engineering workflow
10:16 — Heat transfer, fluid dynamics, and choosing the right wedge use case
11:14 — Vinci’s focus: semiconductor and electronics thermal problems
13:23 — Thermo-mechanical deformation and why materials warp
14:49 — Multi-physics simulation as a long-standing engineering holy grail
16:06 — Yield, reliability, and manufacturing risk in electronics
17:04 — ROI: faster design loops and thousands of analyses per day
19:23 — Uncertainty, validation, and trust in AI simulation
20:08 — Training on 45TB of physics simulation data
21:47 — Residual norms and transparency at inference time
24:42 — 300 million to 1.2 trillion degrees of freedom
25:51 — GPU requirements and why Vinci is built for modern hardware
27:09 — Quantum computing, GPUs, and future scalability
30:22 — Wedge use cases: chips, boards, servers, batteries, defense, robotics, steel plants
31:45 — Who buys AI-native simulation software?
33:50 — Why thermal engineers are Vinci’s first target users
35:06 — Power, cooling, throttling, and data center energy constraints
36:25 — What throttling means in chips, EVs, and thermal runaway scenarios
39:58 — Deployment, IP protection, Docker containers, cloud, and on-prem
41:27 — How to convince skeptical engineers
43:00 — Path to adoption: start with the customer’s real benchmark
44:16 — What engineering leaders should do next
45:47 — The physics brick in the AI factory of the future
46:03 — 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

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.  

Timeline

00:14 — Intro: Adar Hey of Jiga and Or Israel of Bananaz
00:40 — What Bananaz does: AI layer on top of CAD
01:24 — What Jiga does: sourcing custom parts more efficiently
02:20 — Were they bullish or skeptical on AI in 2022?
06:01 — How AI changed the way they build software
10:50 — Token costs, burn rate, and ROI of AI tools
14:22 — Where AI sits in the product stack
18:00 — Off-the-shelf LLMs vs open-source models
20:15 — Bring-your-own-model vs vendor-managed AI
22:22 — Security, SOC 2, and protecting customer IP
26:01 — 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 jobs
35:49 — When will engineering get its “OpenAI moment”?
40:09 — Digital maturity of current customers
42:29 — 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 authoring

Visual execution for the frontline

Connecting PLM, PDM, and MES

Turning video and 3D into interactive instructions

Reducing training time, errors, and scrap

Making the digital thread operational

#DigitalThread #IndustrialAI #Manufacturing #PLM #MES #ConnectedWorker #WorkInstructions #SmartManufacturing #Engineering #FrontlineOperations #AI #AIAcrossTheProductLifecycle

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. 

Timings

00:00 Introduction to Chris Barton and Janis Vavere

00:29 Chris Barton on Drafter and the manufacturing documentation problem

01:04 Janis Vavere on Trace.Space, requirements engineering, and why the category stopped innovating

02:50 The 2022 OpenAI moment: bullishness, skepticism, and early experiments

05:38 Why engineering AI was not good enough three years ago

05:40 Chris on precision, determinism, and why hallucinations are unacceptable in engineering drawings

07:03 How AI changed software development inside Drafter

09:02 How Trace.Space engineers went from skepticism to heavy AI usage

11:10 Where AI is visible inside the product versus buried in the stack

13:53 Deterministic outputs, human review, and reliable AI for engineering

15:54 Whether startups like Drafter and Trace.Space should build their own models

18:18 How these startups coexist with and challenge Siemens, PTC, and Dassault

19:14 Chris on the “gray area” between CAD and manufacturing where work still runs on PDFs, email, and spreadsheets

20:32 Drafter’s 2D strategy and why 2D drawings still dominate the physical world

22:14 Janis on agentic iteration between requirements, drawings, and simulation

23:17 Why legacy requirements tools are frustrating modern engineering teams

25:47 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 disappear

31:21 Infinite design space exploration and what AI is unlocking

32:39 Advice for younger engineers: where to work and how to stay relevant

35:45 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 is

41:09 Customer digital maturity: from Excel-and-email workflows to agent-first engineering

44:22 Does adopting one AI-native tool trigger broader digital transformation?

46:12 The customer epiphany moment after using Trace.Space or Drafter

46:47 Where to meet Janis and Chris at upcoming events

48:00 Closing remarks and AWS sponsorship mention

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

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 management
• The myth of a single version of truth
• Variant chaos and effectivity complexity
• Why most companies still fail at adoption
• AI, product memory, and the future of CM

If 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?

⏱️ TIMELINE

00:00 – Intro + panel lineup
00:09 – 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 lever
• Methodology ≠ success (adoption is the issue)

06:47 – Minimum data model for CM

• Identity, effectivity, baseline, traceability
• Why data governance matters more than tools

10:22 – Where CM actually lives (PLM, ERP, MES, everywhere)

• The “octopus problem” across systems

15:12 – Hardest real-world CM problems

• Variant management = BOM chaos
• Effectivity vs configuration confusion
• Software + firmware breaking traditional models

21:53 – Debate: Effectivity (date vs serial vs lot)

• Why “it depends” is unavoidable
• Safety vs cost trade-offs

24:09 – Configuration rules debate

• 150% BOM vs model-based approaches
• Why rules drift over time

26:10 – Digital thread reality check

• Why duplication is inevitable
• Importance of product identity

30:09 – As-designed vs as-built vs as-maintained

• Where control breaks down (hint: service)
• Why “as maintained” is the weakest link

39:38 – AI in configuration management

• Change impact analysis
• Data structure vs AI hype
• “AI is useless without governed data”

48:55 – When is the ChatGPT moment for PLM?

• Simplicity vs complexity
• People problem vs technology problem
• Product-as-agent concept

59:10 – Final thoughts

• Data governance as the core issue
• Why we’re still having the same debates after 20 years

🎯 Key Takeaways

• There is no single source of truth—only closest approximations
• Variant + effectivity = core chaos engine
• CM failure is mostly organizational, not technical
• AI will help—but only if data is structured and governed
• The real frontier: making CM consumable across the enterprise

📢 Follow / Connect

• LinkedIn: Michael Finocchiaro
• More content: DemystifyingPLM.com
• Events: Threaded! Conference Series

Takeaways

• AI as an accelerator in engineering
• AI empowerment in engineering

Chapters

• 00:00 Introductions and Company Overview
• 06:41 AI as an Accelerator in Engineering
• 13:13 AI Empowerment in Engineering
• 20:44 Integration of Disciplines through AI
• 30:25 Integration of Tools and Platforms
• 37:40 Role of AI in Engineering
  

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

Takeaways

• Narrow focus on specific markets allows for deep integration of AI into industry-specific challenges
• AI has revolutionized the development process in both maritime vessel design and medical imaging, leading to significant advancements
• Regulatory frameworks play a crucial role in shaping the deployment and use of AI in these industries AI's role in medical and maritime industries
• Challenges and opportunities for digital transformation
• Impact of AI on job prospects for younger generations

Chapters

• 00:00 Intrinsic Context and Specificity in AI Models
• 43:36 Digital Maturity in Healthcare and Maritime Industry
• 51:12 Impact of AI on Customer Transformation
  

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

Takeaways

• Early adoption of AI in business
• Impact of AI on software development

Chapters

• 00:00 Introduction to Maintain X and Machine Metrics
• 05:19 AI Integration Across Business Functions
• 11:30 Advice for Younger Generation in AI Era
• 16:50 User Interaction with AI in Software
• 22:04 Future of AI in Manufacturing
• 27:40 Digital Maturity in Manufacturing
• 33:09 Upcoming Trade Shows and Events
  
  #AI #AIAcrossTheProductLifecycle

Takeaways

• MQTT and OPC UA are complementary protocols
• AI needs data to be effective

Chapters

• 00:00 Introduction to OPC UA and MQTT
• 05:47 MQTT and OPC UA Complementarity
• 11:11 The Role of MQTT in Industrial Data Publishing
• 16:11 The Importance of Open Standards and Security
• 32:35 Adapting to New Technology and the Future of AI

#AI #AIAcrossTheProductLifecycle

Takeaways

• AI's Impact on Code Development
• Digital Maturity in Manufacturing

Chapters

• 00:00 Introduction to Prove It Event
• 06:01 Real-time Intelligence Layer in Manufacturing
• 11:01 AI and Agile Development
• 17:25 Challenges with Current Protocols in Manufacturing
• 22:28 Automation and Robotics in Manufacturing

#AI #AIAcrossTheProductLifecycle

Takeaways

• Initial 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.

Chapters

• 00:00 Initial Skepticism and AI Impact
• 06:22 AI Touch Points in Inductive Automation
• 13:31 Digital Maturity and Customer Adoption

#AI #AIAcrossTheProductLifecycle

Takeaways

• Industry 4.0 innovation
• Product development and acceleration
• AI technology adoption

Chapters

• 00:00 Challenges and Collaboration in the Industry

Takeaways

• AI Adoption in Manufacturing
• Continuous Transformation

Chapters

• 00:00 The Power of Live Demonstrations
• 07:35 AI's Role in Code Development
• 13:14 Addressing AI Anxiety
• 18:53 AI Integration in Litmus
• 25:27 AI's Role in Democratizing Technology
• 31:36 Continuous Transformation in Manufacturing
• 37:05 Progression of Digital Maturity

#AI #AIAcrossTheProductLifecycle

Takeaways

• AI Impact on Manufacturing
• Digital Maturity in Industry

Chapters

• 00:00 Digital Maturity in Manufacturing

#AI #AIAcrossTheProductLifecycle

Takeaways

• AI Adoption in Industry
• AI Impact on Process Automation

Chapters

• 00:00 The Evolution of AI
• 06:48 AI in Hiring and Sales Operations
• 12:37 The Future of TD Engine

#AI #AIAcrossTheProductLifecycle

Takeaways

• Evolution of AI from expert systems to LLMs
• Application of AI in engineering and manufacturing
• Data maturity gaps in industries adopting AI

Chapters

• 00:00 Evolution of AI Systems
• 09:42 AI Implementation Challenges
• 20:29 Cultural Transformation for Automation
• 27:38 Future of AI in Engineering

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

Takeaways

• AI's impact on design processes
• Evolution of engineering practices
• Challenges and opportunities for mechanical engineers in AI
• Integration of AI tools within CAD systems
• AI as a means of capturing and preserving critical engineering knowledge AI's role in capturing knowledge
• The need for skepticism and validation when using AI in engineering processes

Chapters

• 00:00 Introduction and Engineer Introductions
• 13:48 The Role of AI in Mechanical Engineering
• 19:52 AI Integration in CAD Tools
• 25:28 AI Tools and Predictive Operations
• 31:05 AI and Knowledge Capture
• 38:04 AI's Impact on Manufacturing and Design Processes
• 44:43 Internal Memory Trained AIs and CAE Influence
• 49:47 Human Expertise in Mechanical Engineering and AI

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

Takeaways

• AI's evolution from speculative technology to a must-have tool for startups
• The use of AI in product development and its impact on productivity and efficiency AI's role in change orders
• The probabilistic nature of manufacturing

Chapters

• 00:00 AI in Manufacturing and Deterministic Results
• 30:55 Building Trust and UX in AI for Manufacturing

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

Takeaways

• Quantum 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.

Chapters

• 00:00 The Future of Quantum Computing and AI
• 35:11 Digital Maturity and Data Readiness
• 41:24 Quantum Computing and Power Efficiency
• 48:13 European Sovereignty and Quantum Computing
• 54:11 Impact of Quanscient on Digital Maturity

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

Takeaways

• AI in development process
• AI for mechanical engineering
• AI and decision-making

Chapters

• 00:00 Integration of AI in Development
  
  #AI #EngineeringAI #CAD #PLM #DigitalThread #Manufacturing #SupplyChain #IndustrialAI #EngineeringSoftware #AgenticAI #AIAcrossTheProductLifecycle #BetterCallFino

Takeaways

• AI's impact on design and manufacturing
• Custom AI models for specific engineering applications Human-AI collaboration
• Adaptability in job markets

Chapters

• 00:00 Introduction to AI in Product Lifecycle
• 08:12 Early Experiences with AI
• 13:33 AI in Code Development
• 24:14 Training Custom AI Models
• 31:20 Challenges in AI for CAD
• 55:55 The Future of Engineering Jobs

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

Takeaways

• AI as a Tool for Solving Customer Challenges
• AI's Evolution in Engineering Software
• The Role of AI in Engineering Collaboration AI in Engineering
• Challenges in AI Adoption

Chapters

• 00:00 Introductions and Backgrounds of the Guests
• 08:19 AI's Impact on Engineering Collaboration and Workflow Automation
• 15:43 Integration of AI in Engineering Software and User Interaction
• 22:21 The Future of Engineering Software and AI Integration
• 30:36 Root Cause Analysis and Collaboration in Engineering
• 39:04 Digital Maturity in Engineering Organizations
• 48:05 Urgency of Centralized Data Repositories
• 53:20 Impact of AI on Engineering Careers

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

Takeaways

• AI in Product Development
• Servitization and AI
• AI in Industrial Environment AI as a Tool for Knowledge Capture
• Societal Impact of AI

Chapters

• 00:00 AI in Industrial Environment and Quality Control
• 42:11 Competitive Advantage of AI in Industry
• 54:09 Ripple Effect of AI Implementation

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

Takeaways

• AI's impact on software development
• The 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.

Chapters

• 00:00 Skills and Use Cases
• 36:57 Managing Digital Workers and Agents
• 43:43 Economics of AI-Powered Solutions

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

Takeaways

• AI as an Accelerator
• Custom Solutions with AI AI adoption in engineering is a culture question
• The importance of data maturity in engineering organizations

Chapters

• 00:00 Introduction and Company Backgrounds
• 07:36 AI Implementation in Product Development
• 15:09 AI Usage in User Interface and Plumbing
• 23:28 Framework of Advise, Assist, and Automate
• 28:51 Addressing Probabilistic Nature of AI in Engineering

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

Takeaways

• AI in Manufacturing
• AI Adoption in Manufacturing AI demand is emerging
• Market behavior varies based on company size

Chapters

• 00:00 Introduction and Company Overview
• 07:24 Exploring AI Tools and Use Cases
• 13:02 AI Autonomy and Workflow Specificity
• 21:30 Guardrails and Contextual Data in Manufacturing AI
• 26:47 Future of AI in Manufacturing
• 32:13 Market Behavior Based on Company Size
• 37:34 Holistic Approach to AI in Manufacturing
• 45:37 Data Connectivity and Digital Maturity Challenges
• 50:59 AI Implementation and Digital Maturity Acceleration

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

Takeaways

• AI in 3D
• AI and 3D Data
• AI in Product Development AI and 3D Integration
• Challenges of Data Access and Integration

Chapters

• 00:00 Introduction to 3D and AI
• 05:28 AI Integration in Development
• 12:14 AI in 3D Product Development
• 24:43 AI and 3D Data Processing
• 30:48 AI and 3D Integration
• 47:32 Challenges of Data Access and Integration

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

Takeaways

• AI Revolution in Manufacturing
• Impact of AI on Development Workflow AI's impact on workforce
• AI's role in manufacturing
• Challenges in AI adoption

Chapters

• 00:00 Focus on Process Industries
• 32:09 The Evolution of AI
• 41:05 Training and Implementing AI Models
• 53:13 Impact of AI on Manufacturing
• 59:18 User Adoption and Industry Challenges

Takeaways

• AI as a Force Multiplier
• Deep Technologies for AI Integration Complexity of CAM and Assembly
• Challenges in Digital Maturity

Chapters

• 00:00 Introduction and Company Backgrounds
• 06:26 AI Integration in Manufacturing and Work Instructions
• 18:03 Deep Technologies for AI Integration in Engineering
• 23:59 The Future of AI in Engineering and Manufacturing
• 43:15 Challenges in Digital Maturity

Takeaways

• AI's impact on productivity
• AI integration in engineering software Realistic approach to AI in engineering
• Importance of fundamental knowledge in engineering education

Chapters

• 00:00 Introductions and Background
• 08:15 AI in Development and Engineering
• 13:38 AI in Simulation and CAD Cleanup
• 20:11 Engineer's Perspective on AI
• 27:27 AI Integration in Engineering Software
• 33:20 The Future of Engineering and AI
• 42:00 The Future of Work and Engineering Education
• 53:33 The Impact of AI on Engineering Workflows

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

Takeaways

• Tribal knowledge in AI
• Challenges of tribal knowledge in engineering and manufacturing AI and tribal knowledge are transforming the way work is done
• The impact of AI on hardware startups and the role of young engineers

Chapters

• 00:00 Introduction to Tribal Knowledge in AI
• 06:05 Defining Tribal Knowledge
• 11:59 AI and Tribal Knowledge Integration
• 17:58 Tribal Knowledge and Agile Methodology
• 23:55 Real-Time Configuration Management
• 29:07 IP and Tribal Knowledge
• 36:03 Differentiating Tribal Knowledge and System of Record
• 41:19 Balancing Quality, Schedule, and Cost in Hardware Companies
• 53:31 The Future of Tribal Knowledge and AI

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

Takeaways

• AI in engineering and manufacturing
• Integration of AI in developer workflow AI is being used to automate and optimize various engineering processes.
• The adoption of AI tools and platforms can lead to improved efficiency, reduced errors, and better decision-making.

Chapters

• 00:00 Introduction to AI in Engineering and Manufacturing
• 05:02 AI Integration in Developer Workflow
• 10:33 Collaboration and AI in Development
• 16:32 AI Integration in Epsilon 3 and Cisco
• 21:44 Customer Collaboration and AI Flexibility
• 30:28 Changing User Experience with AI and Automation

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

Takeaways

• AI-driven simulation software
• Impact of GPT-3 on engineering
• AI's role in the development cycle AI in Engineering
• Digital Maturity
• Data and IT Separation
• Industry Events

Chapters

• 00:00 AI Usage of Foundation Models vs. LLMs
• 33:57 AI in Design Software
• 46:06 Digital Maturity in Enterprise
• 55:15 Upcoming Industry Events

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

Takeaways

• Adoption of AI in process industries
• Challenges and opportunities of agentic AI AI's role in modernizing process industries
• Challenges and benefits of AI adoption in industrial engineering

Chapters

• 00:00 Introduction to Axelir and Montblanc AI
• 05:22 AI in Software Development
• 10:35 Collaboration with AI in Software Development
• 16:01 Centralized Knowledge Base and Memory
• 21:37 Deterministic Nature and AI in Process Industries
• 28:42 Contextualizing AI in Industrial Automation
• 34:30 The Future of AI and Model Providers
• 40:55 Preparing for AI in Industrial Engineering
• 48:01 Benefits of AI Implementation
• 54:09 Engagement and Networking

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

Takeaways

• AI's impact on engineering workflows and development processes
• The distinction between physics-based and surrogate models AI in engineering requires domain-specific models
• AI adoption in enterprises progresses from level one to level five
• AI-native startups have a competitive advantage over big players
• Agility and speed in AI development are key differentiators

Chapters

• 00:00 Introduction and Background
• 06:21 AI-Powered Workflow and Development
• 13:46 AI's Impact on Agile and Development Processes
• 19:12 AI Integration in Key Ward Products
• 25:23 Building Large Engineering Models
• 32:00 The Future of AI in Engineering
• 43:10 Digital Maturity in Enterprises
• 48:26 Impact of AI on Enterprise Transformation

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

Takeaways

• PLM analysts focus on the business value of technology, market measurements, and consulting services.
• AI is seen as a tool to drive conclusions and aggregate information, but it is not expected to replace human analysts.
• The panel discusses the challenges of addressing the future of PLM and the current state of PLM for different companies, as well as the bias within the PLM analyst industry due to the availability of funds for services. Independence of SIMData
• Impact of clients on company policies
• Usage of AI with legacy data
• Role of technology in PLM solutions
• Future breakthroughs in engineering and AI

Chapters

• 00:00 Addressing the Current State of PLM
• 57:20 Future Breakthroughs in Engineering and AI
  

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

Takeaways

• Diverse expertise and experience in the PLM industry
• Discussion on redefining the scope and relevance of PLM The importance of engineering in product development and revenue generation
• The need for organizational change to support PLM initiatives

Chapters

• 00:00 Introduction of Panelists
• 34:27 The Role of Engineering in Business
• 40:24 Elevating PLM with Technology
• 49:18 The Perception of PLM in Organizations
• 58:35 The Evolution of PLM and the Digital Thread

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

Takeaways

• PLM evolution and transformation
• Challenges of legacy PLM systems
• The role of organizational change management (OCM) in PLM adoption Cloud challenges and solutions
• The role of AI in PLM
• The need for a unified approach to PLM

Chapters

• 00:00 The Evolution of PLM
• 08:35 The Role of OCM in PLM Adoption
• 24:27 PLM as an Umbrella Solution
• 29:53 Platform Capabilities for Concurrent Design
• 35:26 Different Types of Cloud Solutions
• 41:12 The Role of AI in Market Growth
• 46:12 The Need for Comprehensive PLM
• 53:06 PLM as a Solution, Not a System
  

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

Takeaways

• Complexity of technology and cost-cutting measures impact engineering excellence
• The need for a robust digital thread to capture and analyze critical data Digital thread and digital twin are essential for real-time monitoring and management of product lifecycle.
• The integration of IoT-enabled black boxes and real-time data can significantly improve safety and performance in the aerospace and automotive industries.

Chapters

• 00:00 Impact of Cost-Cutting Measures on Engineering Excellence
• 57:06 Recycling and Afterlife of Products
  

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

Takeaways

• BOM (Bill of Materials) is a complex and multi-level structure that represents the materials, components, and relationships within a product.
• The use of Excel as a BOM tool is prevalent due to its simplicity and widespread availability, but it may not be the most effective solution for managing complex BOM structures.
• New software solutions and startups are emerging to address the challenges of BOM management, offering more advanced and user-friendly tools for BOM representation and management. Data model is crucial for BOM management
• AI and automation can impact traditional BOM processes
• Start with the business needs and endeavor before building the data model

Chapters

• 00:00 Use of Excel and Emergence of New Software Solutions
• 47:00 Role of AI and Automation in BOM Processes

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

Takeaways

• Change management in PLM involves different types of changes, including engineering change management, enterprise change management, configuration control, and organizational change management.
• The integration of AI and the use of databases play a significant role in the evolution of change management processes. Change Management in the Concept Phase
• Collapsing of Disciplines
• Continuous Change

Chapters

• 00:00 Introduction to Change Management
• 18:57 The Impact of AI and Databases on Change Management
• 31:07 Enterprise Change Management and System Thinking
• 56:12 Collapsing of Disciplines
• 01:01:33 Continuous Change

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

Takeaways

• Digital twins and BIM are closely related to product lifecycle management (PLM) and asset management.
• The construction industry faces challenges in managing digital information across the lifecycle and ensuring the trustworthiness of data. Digital Twin Cost
• Interoperability Challenges

Chapters

• 00:00 Introduction to BIM and PLM
• 09:44 Journey to Expertise in BIM and Digital Twins
• 19:52 Challenges in Construction Lifecycle Management
• 25:51 Asset Management and Maintenance Challenges
• 31:58 Digital Twin in Civil Infrastructure
• 48:38 Manual vs. Machine-Built Buildings

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

Takeaways

• AI's Impact on Engineering
• Integration of AI in Engineering Workflows First principles understanding
• Multidisciplinary engineering

Chapters

• 00:00 The Future of Engineering and AI Integration
• 28:27 The Open AI Moment for Engineering
• 42:41 Parting Thoughts on AI and Engineering

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

Takeaways

• AI in Manufacturing
• Challenges in Manufacturing
• Ease of Use and Trust
• AI Under the Hood AI and physics-informed machining
• Challenges in manufacturing
• The impact of startups in the manufacturing industry

Chapters

• 00:00 Introduction and Company Introductions
• 06:03 The Use of AI in Development and Production
• 12:00 Integration and Distribution Challenges in Manufacturing
• 20:13 The Future of AI in Manufacturing
• 25:13 AI Under the Hood in Manufacturing
• 30:25 Cognitive Robots and Self-Learning Machines
• 40:11 Digital Maturity in Manufacturing
• 45:24 Competing in the Manufacturing Industry
• 51:37 The Future of Work in Manufacturing
  

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

Takeaways

• Prove It conference overview
• Keynote and presentations
• Vendor presentations and categories
• AI and machine learning in industrial automation Integration of OT and IT infrastructure
• The role of Litmus and Fuse in OT and IT integration Red and blue data in the context of 4.0 space
• Challenges and opportunities in industrial automation

Chapters

• 00:00 Introduction to Prove It Conference
• 07:00 Use Cases and Scenarios at Prove It Conference
• 17:20 Presentations on Day Two
• 38:31 Understanding Red and Blue Data
• 50:14 Day Four Highlights and Reflections
• 57:10 Looking Ahead to Next Year's Provence

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

Thank you to all of the attendees of the inaugural The Future of PLM webcast today! Your feedback is much appreciated.

To watch the replay, you can catch it here on LinkedIn at this address:

https://www.linkedin.com/events/7325813120503865344/about/?originTrackingId=ufZzKscKRtin0BsC3uzG8w%3D%3D

or over on my dedicated YouTube channel here: https://lnkd.in/dd7c9jjm

And of course, heartfelt thanks to my co-hosts Elizabeth Graham of Ada IQ and Oleg Shilovitsky of OpenBOM for their brilliant insights!

New episode also here on Spotify!

#DemystifyingPLM #TheFutureofPLM #DigitalThread #DTaaS #ThreadTalk #ConnectedIntelligence #DigitalInnovation #A!

Christine Longwell: “As long as PLM is considered a PLM tool from a vendor, it will never get the corporate attention that it needs to get funding for that information to break out of your engineering system. You need PLM to go through your services group, to manage ideation, to listen to what the customers want.”

Martin Eigner: “My dream, my really dream, is to build up a symmetrical data model—a digital thread above all the legacy systems—so end-to-end processes like release management, change management, configuration management, quality management sit on top of these processes.”

Patrick Hillberg Ph.D. Hillberg: “PLM is to product lifecycle management what IBM is to international business machines—it’s an acronym that no longer means what it meant when it started. If we want to manage product life cycles, we need to figure out how to integrate the databases to create a digital thread.”

Jos Voskuil: “Recently, I’m working a lot with companies and I don’t mention the ‘P word’ anymore...I think we all agree we have to solve business issues and those business issues because of digitalization can now become end-to-end business issues.

Brion Carroll (II): “Introducing the digital thread to people in manufacturing and why they need to look upstream is a conversation I have regularly to find that business imperative… It’s weird that the people who drive business continuity all the way from ideation to on the shelf have the pedigree of PLM.”

Juliann Grant: “PLM doesn’t really get its rightful due when it comes to looking at the C-level. ERP gets the right money and resources, but PLM is tougher because it touches so many places—from incoming marketing, sales, design, to service.”

Jim Brown: “It doesn’t matter what we call it. It’s about what are the business problems that need to be solved. It’s more important about the strategy first and about the business processes, and what vendors call PLM today isn’t enough to fulfill that.”

Full webcast here: https://lnkd.in/eCjgW-sB

#PLM #DigitalThread #BetterCallFino #TheFutureOfPLM #Podcast #A!

Rob Ferrone, Oleg Shilovitsky, Brion Carroll, Brion Carroll (II), Martin Eigner, Jos Voskuil, and Juliann Grant.

We tackled the question nobody wants to answer: Is your PLM system actually solving problems or just creating new ones?

Here's what came out of it:

"PLM should not be a single system. PLM should be a broader set of tools and technologies that manage the product throughout its life cycle." — Brion Carroll

"No one is waking up and saying, I need PLM. Everyone is waking up saying, I want to solve a problem." — Oleg Shilovitsky

"We need something like an umbrella on top of all the legacy systems that breaks the silos and helps us communicate with artificial intelligence." — Martin Eigner

"Companies need to take a step back and not look at technology as a solution, but look at what they're trying to accomplish from a product perspective." — Juliann Grant

The reality?

Half of manufacturers are planning to replace their PLM systems.

Not because the technology is bad.

But because the approach is broken.

→ PLM isn't a vendor's system anymore

→ It's a solution that spans your entire product lifecycle

→ From ideation to service and back

→ AI can't work without it being unified first

We're working on a PLM Manifesto to define what this actually means.

If you're dealing with legacy PLM systems, integration nightmares, or trying to figure out where AI fits...

This conversation is for you.

Want to be part of the next discussion?

💬 Comment "MANIFESTO" below and I'll keep you in the loop

♻️ Repost this so your network sees where PLM is actually headed

The future of product development isn't about better systems.

It's about smarter solutions.

📺 Full recording here: https://youtu.be/sjsKxKf0Q_8?si=FujONeOk8lnlqAQL

#PLM #ProductLifecycleManagement #DigitalTransformation #AI #Manufacturing #BetterCallFino #TheFutureOfPLM #AI

In this latest roundtable, I had the pleasure of hosting another international group of PLM gurus:

🇳🇱  Jos Voskuil (Tacit / Virtual Dutchman blog)

🇬🇧  Valentina Futoryanova (AVEVA)

🇩🇪  Rob Ferrone (Quick Release_ / PLM Plumber)

Together, we dove deep into Asset Lifecycle Management (ALM) — where the real value of PLM often emerges after the product is built.

Some of the key themes we tackled:

🏷️ Why tag-based data models are critical for ALM

🏭 Challenges with handover from EPCs to operators

🧠 How AI and digital twins are reshaping the future of configuration management

⚙️ The debate: Is PLM a system… or an infrastructure?

🔂 The shift from “Single Source of Truth” to “Nearest Source of Truth”

⁉️ Will we even need PLM systems, or will AI agents take the lead?

This session was packed with actionable insights, spirited discussion, and real-world examples across nuclear, marine, defense, and infrastructure.

📺 Catch the polished version (with intro/outro and smoother transitions): https://lnkd.in/eCGrij6K

🎧 If you prefer to listen to this hour of tidbits during your commute or while you are out and about, it is also on Spotify here:

https://lnkd.in/eWFa22nq

🔮 Next up: SharePLM Summit Debrief!

We’ll bring you the highlights and what they mean for the future of neutral, community-driven PLM.

Stay tuned — and bring your questions!

#PLM #AssetLifecycleManagement #DigitalThread #DigitalTwin #EngineeringData #AVEVA #ArasInnovator #QuickRelease #TacIT #Industry40 #DataCentricity #AI #Interoperability #Aras #BetterCallFino #DemystifyingPLM #ConnectedIntelligence #ThreadTalk #IIoT #FutureOfWork #SmartIndustry #PLMCommunity #SharePLM