A practice's guide to evaluating AI document extraction: 8 questions to ask
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Handling errors and uncertainty
Ask what happens when the AI gets a value wrong. A useful answer should explain how the issue is identified, what a reviewer can change and how that decision is recorded.
Then ask how uncertain documents are handled. The workflow should make it clear when extra attention is needed rather than obscuring uncertainty behind a completed-looking result.
Transparency for every review decision
Can you see a confidence score for each extracted value? Confidence information is most useful when it helps a reviewer decide where to focus, not when it is presented as a guarantee.
What does the audit trail capture, and can it be exported? Understand which edits, approvals and status changes are retained so that the practice can support its own governance processes.
Commercials and line-item extraction
Is line-item extraction included, or is it metered? Ask for a clear explanation of how usage is measured, what is included and where additional charges could arise.
A commercial model should be understandable alongside the workload it supports, particularly where document detail is central to a practice’s review process.
Integrations and the roadmap
Which accounting software does the tool integrate with, and how does the connection work? Look for practical detail on account mapping, hand-off points and the records available after a push.
Ask what is on the roadmap and whether future work is clearly separated from live functionality. This helps the practice make a decision based on the product available today.
GDPR and data security
How are documents and personal data handled? A provider should be able to explain its approach to data access, retention and the controls available to customers.
For UK practices, evaluate those answers against your own GDPR responsibilities and the information your clients expect you to protect. Treat this as a core part of due diligence, not a final checklist item.