🚨 PLM just had a major market signal.

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

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

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

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

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

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

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

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

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

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

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.