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

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