I work at the edge of PLM and Digital Manufacturing — not because it was the path of least resistance, but because it's where some of the most complex and underappreciated engineering problems live. And now I'm asking: what changes when you bring AI into this?
I'm a PLM Technical Consultant who started with mechanical engineering and ended up deep inside enterprise product lifecycle systems — configuring workflows, structuring product data, and making sure engineering teams can actually use the tools built for them.
My core domain is Dassault Systèmes' 3DEXPERIENCE ecosystem — ENOVIA, CATIA, DELMIA — across automotive and aerospace environments. I've done the pre-sales demos, the full implementations, the server configurations, the user training. I understand how these systems behave in theory and, more importantly, how they behave in practice.
But curiosity doesn't sit still. PLM systems are sitting on enormous amounts of engineering data — and most of it is either underused or completely ignored. That's the gap I'm trying to close — using AI and machine learning to make digital manufacturing smarter, more predictive, and more useful.
When I'm not working on that: building side projects, writing about PLM and Industry 4.0, and pursuing an MS in AI/ML at Woolf University.