Beyond PLM

Why AI needs the product situation, not only the product definition Imagine an engineer asking an AI assistant a simple question: can we replace this motor with an equivalent model? The assistant compares power, speed, torque, dimensions, voltage, and mounting interfaces. It finds a motor with nearly identical technical characteristics and produces a confident recommendation. On the surface, this is exactly the kind of work AI should perform well.

[HERO IMAGE] I have been writing about Product Memory for some time now, developing the idea that companies need more than product data, files, BOMs, revisions, and change records. They need to preserve the context of the product: what it is, how it changed, why decisions were made, and who was involved. Last week I found that this idea is starting to live its own life.

The conversation about Product Memory reached an important milestone last week. For the first time, the concept started traveling without me. Brion Carroll (II) published a post titled “Why AI Needs Product Memory“, making the argument from the AI side: an AI system can summarize documents and search information, but it cannot understand decades of engineering decisions, supplier trade-offs, design intent, and manufacturing lessons if that knowledge is fragmented across systems and people.

As I’m moving forward in the exploration of Product Memory development and working on my book, From CAD Files to Product Memory, one question keeps coming up in the discussion. It is about the invention of new buzzwords and names.

Doug Macdonald published a report in July 2026 called “The Future of Product Lifecycle Management: After 40 Years, Where Are We And What’s Next?” It argues that legacy PLM has failed, proposes a successor category called the Future Platform for Hardware Engineering, and evaluates eight emerging vendors against that model. I appreciate Doug’s dedication to the problem and his experience. Being a frequent commentator of my posts and ideas, I think Doug always brings an interesting perspective.

After my keynote at the IFIP PLM 2026 conference in Lecce earlier this month, someone in the audience asked me the question I put in the title of this article. I think it is an important question and it requires discussion and some historical perspective on both CAD/PLM industry development and AI development. The first is 40+ years old, going back to the first CAD files and the PDM systems invented to manage them.

I continue my article series about IFIP PLM 2026 in Lecce, Italy. If you missed my previous articles, check them out here: [IFIP PLM 2026 in Lecce: Digital Thread, AI, and a Community Thinking About What Comes Next] and [40 Years of PLM: From Documents to Intelligence — Martin Eigner’s Pioneer Keynote at IFIP PLM 2026]. Today I want to share my own keynote, “From CAD Files to Product Memory”: the argument behind it, and the reaction from the industrial and academic community.

This is the second article in my series about the IFIP PLM 2026 conference in Lecce, Italy. Today I want to talk about one of the most anticipated talks of the conference – the PLM Pioneer keynote by Prof. Martin Eigner: “40 Years of PLM” If you’ve been in this industry for any length of time, you know Martin Eigner.

Last week I attended the IFIP 23rd International Conference on Product Lifecycle Management (PLM26) in Lecce, Italy, hosted by the University of Salento and its Department of Innovation Engineering. Three days (July 6-8), a doctoral workshop, industry day, keynotes from academia and industry, a digital twin contest, and, honestly, one of the best settings a PLM conference could ask for — the baroque streets of Lecce in July.

On Monday, a group of us will sit down for Part 2 of the Future of PLM BOM debate. Michael Finocchiaro pulled the panel together, and the lineup is a good one: Brion Carroll, Patrick Hillberg, Rob Ferrone, David Schultz, and me. Part 1 asked why engineering and manufacturing still struggle to speak the same language. Part 2 asks a sharper question. What if manufacturing reality should drive engineering more than engineering drives manufacturing?