Human review belongs in document AI from day one
Why document AI projects in healthcare, insurance and government should design human review first, and how per-field confidence thresholds make it work.
Read articleSeven questions to ask before buying an enterprise AI tool
A practical checklist for evaluating AI vendors: testing on your own documents, data residency, citations, human oversight, monitoring and exit plans.
AI vendor demos are designed to impress. The documents are clean, the questions are well chosen and everything works. Your documents will not be that tidy. These are the questions I use when helping organizations evaluate enterprise AI tools.
1. Will you test it on our documents?
Ask for a proof of concept on a sample of your real files, including the messy ones. Agree the success measures before the test starts, not after the results come in.
2. Where is our data processed and stored?
For regulated organizations in the UAE, data residency and handling are often decisive. Ask where data is processed, where it is stored, how long it is kept and whether it is used to train models.
3. How does it show its work?
For search and question answering, answers should cite the source document and page. For extraction, each value should link back to the text it came from. If a reviewer cannot check an answer quickly, they will either trust it blindly or stop using it.
4. What happens when it is unsure?
Look for confidence scores, review queues and a clear handoff to a person. A tool that always sounds confident is a risk, not a feature. I wrote more about this in why human review belongs in document AI from day one.
5. How will we measure quality after go-live?
Accuracy in the pilot is not accuracy in month six. Ask what monitoring is included, who owns it and how issues are reported.
6. What does it integrate with?
Most of the value comes from connecting to systems you already run. Ask for specifics: which APIs, which authentication methods, and what it costs to add an integration later.
7. What are the service levels, and the exit plan?
Get uptime, support response times and incident processes in writing. Ask how you would export your data and configuration if you leave.
Turn the answers into a score
Build these questions into a weighted scoring matrix before you meet any vendor, so every proposal is judged the same way. Weight each criterion by what matters to your organization: a hospital may put data handling above cost, while a startup may do the reverse.
Need help writing the requirements or running a fair evaluation? Get in touch.