Introduction

"In the real world, it is very rare for engineering or manufacturing data to be truly ‘clean’! However, as product complexity and variability increase, fragmented and incomplete data relationships proliferate at an exponential rate. And that’s true in virtually every industry now.

By leveraging AI, PLM solutions can maintain digital continuity and run core business processes reliably, effectively simulating integrated data environments even when the underlying sources remain fragmented."

Vincent Garsuault | PLM Center of Excellence
Chief Architect, Capgemini Engineering

A lot has been written about the technicalities of applying AI to PLM, but much less about the specific value it can bring. However, before a company can make the most of AI-powered PLM, it must first pinpoint the issues where AI is most relevant to its own PLM activities and quantify the potential of AI in each case.

This POV is designed to help our clients do exactly that. First, we identify the principal PLM issues to which AI can contribute. Then we examine the value that AI can add in each case. We do this by defining four pillars – four major ways in which AI can deliver value in the PLM context. We also outline a hybrid operational model for collaboration between humans and AI agents.

In later articles, we’ll discuss each of the pillars in detail, and recommend specific AI-powered solutions.