What is the exact task?
Writing a draft description, suggesting a match and making a final decision are different activities. Ask the vendor to describe the input, the output and the action that follows. If the explanation ends at 'AI-powered', keep asking until you can describe the task in ordinary language.
Consider a fictional operations example: a tool reads a label and suggests a recipient. The relevant questions include which fields it reads, whether it can leave a field blank and who confirms the result. A polished demonstration of a clear label does not show what happens when the photograph is blurred or two people have similar names.
What evidence can the person inspect?
Ask whether the person using the feature can compare the suggestion with its source. That might be the original photograph, a linked publication or the record used to produce an answer. A result is easier to review when the relevant evidence is close to it.
For research talent discovery, a name or job title alone may be insufficient for the question a team is trying to answer. ForesIQ, a Naltyx product, brings academic and career information into a searchable database. When assessing any research tool, inspect the underlying work and distinguish recorded information from an inference you make yourself.
Who makes the decision?
Find the exact point where a suggestion becomes an action. Is there a review step? Can a user change the draft? Does accepting it send an email, change a record or affect somebody's access? The consequence matters more than the visual style of the AI assistant.
Ask whether permissions still apply to the feature. A person who cannot see a private record through the normal interface should not gain access merely by asking an assistant about it. Treat this as something the vendor should demonstrate for your roles and data boundaries, rather than a promise you infer from the presence of a login.
What happens when the input is poor?
Use fictional examples with missing fields, ambiguous wording and information that conflicts with an existing record. Include a case where there is not enough evidence to answer. Check whether the tool asks for clarification, leaves the result unresolved or produces a confident-looking guess.
Write down what you would expect a colleague to do next. An error message should lead somewhere useful: retry with a clearer image, correct a field, review the source or complete the task manually. If nobody can explain how the work continues after an error, the trial is not finished.
Where does the information go?
Read the product's current privacy and data-processing information. Ask which providers handle the input, what is retained, who can access it and how deletion works. Avoid putting sensitive real records into an evaluation until the answers fit your requirements.
These questions need product-specific answers. A feature's description does not establish its retention policy, and the policies of one product do not automatically apply to another product from the same company. For Naltyx's products, follow the privacy and processing documents on the relevant product website.
Keep a short review record
- Task and consequence: what does the feature suggest, and what changes after acceptance?
- Evidence: what can the reviewer inspect?
- Control: who can use it, approve it and correct it?
- Failure: what happens with ambiguity, missing information or a service error?
- Data: which current documents explain processing, retention and deletion?
Repeat the review when the task or its consequences change. A feature that only drafted text during a trial may later gain the ability to act on records. That is a different decision to evaluate.
AI can be one part of a useful workflow. The purchasing decision should still rest on the work your team can verify, the control it keeps and the route it has when a suggestion is wrong.
Published by Naltyx. Examples are illustrative; any Naltyx product mentioned is our own. How we write.