Factory AI lives or dies on data plumbing and trust on the floor. Predictive maintenance and vision inspection deliver real money — but only after sensors are calibrated, failure data is labeled, and operators believe the alerts.
Why: Most failed factory-AI projects die here: the model is fine, but the data can't distinguish a bearing failure from a sensor drift or a shift change.
Why: One credible win — 'we caught the gearbox failure 9 days early, saving 14 hours of line stoppage' — buys more adoption than any executive mandate.
Why: The failure mode of plant AI is social: three false alarms with no explanation and the crew stops looking. Feedback loops keep precision — and trust — improving.
Why: Vision models degrade silently with environmental drift. A golden set plus sampled audits catches the decay before customers do.
Why: Probabilistic systems can't be safety-certified as-is, and one AI-attributed injury sets plant-floor trust back years. Advise-then-act preserves both compliance and adoption.
Manufacturing AI case studies celebrate the model; practitioners know the project is mostly plumbing. Getting calibrated, consistently-named, timestamp-aligned data out of a 15-year-old line — and building the habit of labeling every failure event — is the actual work. Teams that accept this upfront ship in months; teams that don’t restart the project twice.
Generative AI has a real but different role on the floor: turning maintenance manuals, work instructions, and tribal knowledge into a searchable assistant for technicians (grounded, with citations to the manual page), and drafting shift reports. It complements, rather than replaces, the sensor-driven analytics above.