AI Hallucinations in Manufacturing

Fault reports, work instructions, drawings, and manuals are scattered across languages, sites, and systems, often without reliable version control, approval status, or a clear record of who signed off on what. When AI reads that landscape, it can answer confidently from an outdated or unapproved copy without recognizing the problem.

This session explores how gaps in documentation can contribute to AI hallucination in manufacturing. When revision details, approval status, and signatures are captured in document metadata, AI systems can use that context to identify the right information and support more reliable answers.

Drawing on real deployments already running today, we’ll show what this foundation makes possible: detecting fault patterns across languages and a global installed base, and predicting delivery risks across engineer-to-order projects that share the same milestone structure despite differing engineering.

What You’ll Learn

• Why unreliable manufacturing documents can lead to inaccurate AI answers, even when the model itself is capable.
• How version control, approval chains, and metadata help create a trusted information foundation for AI.
• How that foundation supports cross-site fault correlation and milestone delay-risk prediction in real deployments.