An AI label does not tell you whether a transaction tool can extract facts, summarize documents or merely trigger predefined reminders. Evaluate the exact function and its failure behavior before building a workflow around it.
Make the vendor define the AI task
Require a demonstration of the proposed task. Distinguish rules-based date reminders from model-generated interpretation, and a draft suggestion from a final action. Identify the product plan, configuration and limitations behind the demonstration.
Challenge every candidate with the same difficult file
Include an amendment, a missing exhibit, a poor scan and conflicting amounts. Ask each candidate for the same output and source evidence. Check whether the tool identifies uncertainty instead of inventing a value. Compare unedited outputs, not only the polished vendor demonstration.
In a hypothetical comparison, tool A produces a concise summary without source passages, while tool B cites a page but marks the closing date unresolved because two documents conflict. For a workflow requiring verifiable extraction, B’s uncertainty is preferable to A’s confident unsupported date. This is not a test result for any named product.
Inspect data permissions and downstream actions
Ask where inputs and outputs are stored, whether they are used for training, which subprocessors receive them and how access is revoked. Test whether a suggested field can send a notice or change a material term without the required approval. Reject unclear answers rather than assuming a standard security model.
This article does not rank vendors or confirm current AI features in any platform. Tool behavior can change, and a successful demonstration does not establish legal suitability or production reliability. AI cannot ensure regulatory compliance.
Plan for behavior changes after procurement
Record the tested model or feature version, prompt and configuration where available. Ask about change notification, logging, rollback and exports. Re-run the sample after material changes and price the review effort as part of the tool’s cost.
- Exact AI task and plan identified.
- Same exception-heavy sample used.
- Sources and unknown states inspected.
- Data use and action permissions documented.
- Version changes trigger re-evaluation.