What happens when some of the most respected voices in PLM gather in a Spanish vineyard to discuss AI, digital transformation, trust, community, and the future of engineering?

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

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

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

• 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

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

AI in manufacturing does not fail because the demo is bad.

It fails when the answer cannot be trusted.

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

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

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

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

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

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

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

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

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

What happens when AI moves beyond chatbots and starts reshaping the actual tools engineers, architects, and designers use every day?

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.

What happens when AI, virtual reality, and spatial computing move beyond demos and start reshaping real engineering work?

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro speaks with Jay Wright, Co-Founder and CEO of Campfire, and Oluwaseyi “Shay” Sosanya, Co-Founder and CEO of Gravity Sketch, about the future of immersive engineering workflows.

This is not a “metaverse” conversation. It is about what spatial tools can actually do for product development, design reviews, manufacturing validation, training, collaboration, and digital transformation.

Jay explains why AI is becoming a first-class user inside Campfire, acting almost like another participant in a 3D workspace. Shay breaks down why Gravity Sketch keeps humans at the center of the design process while using AI to remove friction, speed iteration, and help teams communicate better.

The conversation covers the hard parts too: why LLMs still struggle with geometry, why industrial companies remain cautious about cloud and AI adoption, why employees are already using AI tools outside official policy, and why the next breakthrough in engineering may not be AI replacing CAD, but AI controlling and accelerating the tools engineers already use.

For anyone working in CAD, PLM, industrial AI, digital thread, manufacturing, design, or engineering software, this is a sharp look at where spatial computing is actually useful and where the hype still needs to become workflow value.

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

Transcript source:

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 Title
Product Memory: The Missing Layer Between PLM, Digital Thread, and AI Agents

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

Riverside Event Title
Physics Has a ChatGPT Moment: AI, Simulation, and the Future of Engineering

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

What happens when AI hits both sides of the engineering equation: design and sourcing?

In this episode of AI Across the Product Lifecycle, Michael Finocchiaro sits down with Adar Hey, CEO and co-founder of Jiga, and Or Israel, CEO and co-founder of Bananaz, for a grounded discussion on where AI is actually creating value in engineering right now. Bananaz is building an AI layer on top of CAD to automate manual engineering work, while Jiga is rethinking custom part sourcing with software, supplier intelligence, and AI-enabled operations.

This is not a hype piece. The conversation gets into the real tradeoffs: where LLMs help, where deterministic workflows still matter, how engineering startups are using AI internally to ship faster, how customers think about ROI, and why security, traceability, and IP protection still make or break adoption. It also explores a bigger question: when will engineering have its true “OpenAI moment”? Adar argues adoption in physical industries takes time even when the technology is ready, while Or says the shift is already underway and could become unmistakable in 2026 to early 2027.

One of the strongest parts of the episode is the discussion around digital maturity. Both founders place many target customers around a 2 to 3 out of 5: digital enough to understand the value, but far from autonomous or agentic. From there, the discussion turns practical: how do you introduce change without breaking habits, and how do you prove business impact across engineering, manufacturing, and supply chain?

If you care about CAD copilots, sourcing automation, engineering productivity, AI in industrial software, startup execution, and the future of digital engineering, this episode is worth your time.

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

What happens when engineering teams suddenly have the equivalent of 10,000 new AI coworkers?

In this Day 2 Threaded Miami session, Daan Goossens of CoLab lays out a sharp version of the problem: AI can massively increase productivity, but if companies do not have the right collaboration, context, and decision-making structure in place, they will not scale output. They will scale chaos. That framing runs through the entire talk and makes this one of the clearest strategic discussions from the event.

Daan explains that CoLab’s vision for the future is not about replacing engineers. It is about helping humans do more with more, pairing human judgment with AI agents and the right engineering context. His point is that the engineer remains accountable, especially in safety-critical industries, but AI can dramatically expand what teams are capable of if it is embedded responsibly.

He reduces the path to scaled engineering productivity down to three ingredients: human collaboration and decision-making, strong AI agents, and relevant context and data. Miss one of those and the whole thing breaks. That is why CoLab is building around engineering collaboration first, especially design review, where teams already need to bring together multiple stakeholders, surface issues, and make decisions quickly without drowning in screenshots, PowerPoints, email chains, and Teams messages.

Daan also gives a concrete look at where CoLab is going next. He shows how their design engagement system and AI reviewer, Otto, are evolving into a broader engineering operating system strategy. One especially strong example is the SimScale partnership proof of concept, where a user can request a static load analysis on a crane truss, let the AI build and review the simulation plan, run the simulation, and then bring the results back into CoLab for collaborative review alongside the rest of the design context. The broader message is clear: engineers should not have to keep jumping between disconnected tools just to understand the impact of a design decision.

This is less a product demo than a thesis on where engineering software is headed. CoLab is betting that the future belongs to platforms that can combine collaboration, AI, and engineering context in one place, while partnering with the best core tools rather than trying to rebuild everything themselves.

If you care about AI in engineering, design review, simulation workflows, or the emerging idea of an “engineering OS,” this episode is worth your time.

#ThreadedMiami #CoLab #EngineeringAI #DesignReview #Simulation #DigitalThread #IndustrialAI #ProductDevelopment #EngineeringOS #ManufacturingTech #AI