Collect public examples of manufacturing AI failure modes

[programmablemanufacturing]

Many manufacturing AI projects fail for reasons that occur before model performance becomes the main issue.

This issue is for collecting public, generalized, or non-confidential examples of common failure modes.

Examples could include:

• unclear problem definition
• inconsistent data trail
• no actionable decision point
• no operator adoption path
• no feedback loop
• process instability
• unrealistic expectations
• model output not connected to workflow
• unclear ownership between engineering, quality, operations, and data teams

The goal is to build shared language around why manufacturing AI succeeds or fails in practice.

Useful contributions could include:

• public examples from papers, talks, reports, or general industry experience
• generalized observations without company names or sensitive details
• additional failure-mode categories
• suggestions for how to connect failure modes to readiness criteria

Please do not share confidential company information, customer names, private datasets, or proprietary production details.