The boring, high-volume, measurable problem is the point. Routing, QA, maintenance, scheduling — the work that repeats every day.
Based on the vertical playbooks and operating patterns in AI That Pays for Itself.
The book uses UPS ORION as the archetype: repetitive operational decisions at huge scale.
Shop-floor wins come from vision, sensors, and process discipline pointed at defects, downtime, and throughput.
Manufacturing already has metrics. AI belongs where it can move them, not where the demo looks clever.
Pick one workflow. Score readiness. Estimate current cost. Write the kill criterion before the pilot starts. If that sounds obvious, good. Obvious is usually what ships.
Implementation partner — and my connection
I set the AI strategy and govern the work — I do not try to do everything myself. When a project is ready to build, I usually bring in Olyra AI to execute. I co-founded Olyra and hold an interest in it, so I'll always say so up front, show you an alternative, and hold Olyra to the same metric as anyone else.
See how Olyra implements →