Generative AI in industry: deployment guide

Generative AI has left marketing departments and entered the shop floor. Work-instruction generation, failure-diagnosis assistance, automatic technical documentation: industrial use cases are now mature. This guide presents the deployment method that protects your data and your certifications.

First principle: in an industrial environment, the LLM does not decide — it assists. Working architectures place generative AI as a copilot to the operator or technician, with systematic human validation on any action affecting production. This rule is not a transitional precaution: it is a functional-safety requirement and, increasingly, a compliance one.

Second principle: sovereignty of production data. Your routings, process parameters and maintenance histories describe your know-how — sending them to a public API means documenting your competitive advantage at a third party. On-premise or private-cloud deployment is the norm for these uses, and today's open models allow it without sacrificing performance.

Third principle: integration with existing systems (CMMS, MES, ERP) is where the value is. An assistant that answers in natural language but cannot read your production data remains a gadget. Designing secure read connectors, with request logging, is half the project effort — and most of the ROI.

Finally, change management: technicians adopt generative AI when it removes their irritants (document search, report writing) without threatening their expertise. Involving teams from use-case selection onwards is the best predictor of success we observe in the field.

A particularly profitable and low-risk use case to start with: the maintenance document assistant. Connected read-only to your manuals, routings and intervention histories, it answers technicians' questions in natural language and cites its sources. No action on production, immediate ROI on document-search time, and an excellent gateway to acculturate teams before more engaging uses.