A strong AI use case starts with a measurable business process, not a tool. Map current steps, volumes, delays, errors and people involved before choosing a technology.
Assess value, feasibility and risk
- Measure time, quality, capacity or revenue impact.
- Check data availability and system access.
- Define the impact of errors and human approvals.
- Confirm that a real user owns the process.
Select the simplest viable architecture
The right answer may be conventional automation, an assistant, a copilot or a supervised agent. Test a limited scope and compare results before expanding.
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