Choosing a legal AI product is not simply a feature comparison. Legal teams should evaluate how a tool performs on real work, what happens to sensitive information and whether its outputs can be reviewed efficiently.

1. Define the workflow

Identify the task, users, expected inputs and consequences of an error. A tool used to summarize internal documents has a different risk profile from one that drafts material for court filing or advises on a client matter.

2. Test with representative materials

Use a controlled evaluation set that reflects the documents, jurisdictions and edge cases the team actually encounters. Compare outputs with expert-reviewed baselines and record both errors and omissions.

3. Review security and contractual terms

Examine data retention, training use, encryption, access controls, audit logs, subprocessors, incident response and deletion commitments. Confirm that contractual terms match the organization's confidentiality and security requirements.

4. Plan for human review

Define which outputs require approval, how sources are checked and how users report problems. Training should make limitations visible rather than encouraging blind reliance.

5. Measure value over time

Track time saved, quality, rework, user adoption and risk events. Reassess when vendors change models, features or terms. A tool should earn its place through measurable performance, not a compelling demonstration.

Editorial note: This article is educational and does not constitute legal or technology procurement advice.