Season 1. Episode 12. Unscripted with Juergen Weichenberger

Juergen Weichenberger has been building AI since the 1990s, well before it became a mainstream conversation, in domains where the cost of being wrong isn't a typo, it's a shutdown: factories, energy grids, and critical national infrastructure.

 

Having led AI at Schneider Electric and now serving as a data and AI partner at EY, he tells Jeff Pedowitz why the industrial sector remains the least AI-penetrated part of the economy despite having some of the highest value at stake, tracing it back to a fundamental trust gap between engineers and AI teams.

 

The conversation moves through why unconstrained optimization can push a system to a dangerous edge, illustrated by a real example of AI-optimized throughput collapsing a company's own market margins, and why a widely repeated use case like predictive maintenance often adds zero value once you understand how much redundancy engineers already build into critical systems.

 

Weichenberger walks through a real project where 150 AI use cases recommended by a major consulting firm collapsed to a single viable one the moment they were validated against actual operations staff, and closes with what he considers the most underrated skill in AI right now: context engineering, and simply talking to the people doing the work before proposing a solution to a problem they may not actually have.