From Design to Manufacturing: The Engineering Foundation AI Still Needs

Engineering organizations across the industrial equipment sector are facing a compounding problem: experienced engineers are retiring faster than their institutional knowledge can be captured, legacy digital ecosystems can't talk to each other, and AI initiatives are stalling — not because the technology isn't ready, but because the engineering data beneath it isn't structured well enough to be useful.

The result is predictable: unexplained backlogs, AI tools that hallucinate because they're working from fragmented inputs, and a widening gap between what the engineering organization promises and what it can deliver.

In this session, engineering and technology leaders from Dassault Systèmes explore how a structured semantic engineering foundation — built on Model-Based Systems Engineering principles and connected to manufacturing — changes this picture. Not by adding another tool to an already fragmented stack, but by giving AI a reliable foundation to work from.

We'll cover:

• How knowledge drain is accelerating the cost of engineering complexity — and what a structured digital model does to slow it
• Why AI reliability in engineering is a data architecture problem before it's a technology problem
• How MBSE connects upstream system architecture to downstream manufacturing execution, closing the gap where most schedule risk lives