1. Take stock of your document estate
Before picking a vendor, list your information sources: DMS, NAS, network shares, Google Drive, Dropbox, executive mailboxes, PST archives, scans still on paper. For each: estimated volume, dominant format, time period covered, internal owner, sensitivity.
This inventory takes half a day. It will save you two months of later course-correction.
2. Pick a pilot that scares no one
The ideal pilot has three properties: it’s useful to the team, it’s not critical if it errs, and it has a reasonable volume (between 200 and 2,000 documents).
Three examples that work well:
- Onboarding assistant: feeds new hires with procedures, welcome handbook, HR FAQ.
- Doctrine assistant (accounting firms, legal): fed with the technical knowledge base, for internal questions.
- Catalog assistant: products, technical sheets, general terms — useful for sales and support.
To avoid as a pilot: anything touching customers or finance directly. You’ll build those assistants in a second wave, once the team trusts the tool.
3. Frame compliance before tech
Five questions to ask yourself before ingestion:
- Are there personal data in the planned documents?
- Are there sensitive data (health, minors, criminal)?
- Is the vendor’s hosting in France, under French jurisdiction?
- Does the vendor commit in writing to not train its models on your data?
- Is the DPA (GDPR article 28) signed before ingestion?
If any answer is “I don’t know”, stop everything and clarify. More detail on our security & GDPR page.
4. Mobilize three people, not ten
An AI assistant project succeeds with three roles, not a steering committee of fifteen:
- A sponsor (executive or operations lead) who arbitrates trade-offs and unblocks document sources.
- A business lead who knows the content and will validate answers during the acceptance phase.
- A technical lead (IT or provider) who handles access, SSO authentication, ACLs.
No need for more on the pilot. Add the rest at the rollout phase.
5. Go / no-go criteria for scaling up
After three to six weeks of pilot, you should be able to answer yes to these five questions before rolling out:
- Does the assistant give correct answers in more than 85% of tested cases?
- Does it systematically cite the source used?
- Does it politely decline when the information isn’t in its base?
- Does the pilot team use it spontaneously (at least 5 sessions per user per week)?
- Have you measured a measurable time gain on the pilot case?
If a single answer is “no”, go back to pilot. Don’t roll out. Details in Making an AI assistant deployment succeed.
Typical schedule for a well-prepared deployment
Three months between decision and a stable deployment, with measured results at each step. Faster, and you’re cutting corners on quality; slower, and prep was probably skipped.
Twenty-minute demo on one of your real documents. You walk away with a sized pilot case and a six-week roadmap.
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