An ERP contains critical operational data. AI integration should separate language understanding, business rules and technical execution, with least-privilege access and full logging.
Read before writing
Begin with status lookup, explanation and summaries. Introduce write actions only after validation rules, thresholds and rollback paths are proven.
Integration options
- Documented ERP APIs
- An orchestration layer such as n8n
- Read-only data replication
- RAG for procedures and unstructured documentation
Move from an idea to an operational scope
Document the current process, monthly volume, users, systems, available data, common errors and expected outcome before selecting a model. This baseline makes value measurable and exposes missing data or ownership.
Set autonomy according to risk
Start with read-only access or a draft submitted for human approval. Allow execution only for clearly authorised and reversible actions. Sensitive, uncertain or exceptional cases should be escalated with their context and sources.
Test and measure in production
- Quality on a representative test set.
- Processing time and manual rework.
- Tool errors and human escalation rate.
- Actual adoption by the intended users.
- Cost per completed and accepted operation.
France Num recommends a progressive approach based on concrete business needs, clear objectives and employee involvement. Official guidance: https://www.francenum.gouv.fr/guides-et-conseils/intelligence-artificielle/comprendre-et-adopter-lia/comment-deployer-lia