Industrial AI must survive the production line

1. Name the economic bottleneck

Define the loss before selecting the model. Yield, scrap, unplanned downtime, cycle time and energy costs demand different evidence and counterfactuals. Translate the use case into a baseline that the plant and finance teams can both recognize.

2. Reconcile data with the physical process

OT timestamps, sensor calibration, production orders, recipe versions and shift conditions are not administrative details. They define whether two records describe comparable situations. Record missingness and data provenance; do not mistake sensor availability for decision-ready information.

3. Keep action authority explicit

An AI suggestion is not permission to change a live process. Define a named decision owner, control limits, escalation conditions and approved paths for production action. Critical automated actions require appropriate safety and operational engineering, not just a dashboard warning.

4. Measure before and after in a defensible way

Compare like-for-like products, shifts and seasons. Examine alternative causes: maintenance campaigns, operator changes or throughput shifts can confound before-and-after numbers. Report deployment cost, data engineering and staff time alongside claimed benefits.

5. Design for version changes and exit

Plants outlive models and often suppliers. Request data export, interface documentation, rollback, audit logs and operating ownership. A successful proof of concept should leave behind repeatable operations rather than a dependence on an individual project team.

Primary sources and limitations

These links support the public product or research context. They do not substantiate comparative performance conclusions. Source content may change; verify directly with the supplier.

1. https://www.nist.gov/publications/2026-roadmap-artificial-intelligence-and-machine-learning-smart-manufacturing
2. https://www.rockwellautomation.com/en-us/products.html