Most of us get asked to evaluate an AI tool long before anyone asks us to build one, usually by a founder or an ops lead who already picked a favorite. The questions that decide whether a tool survives the year are engineering questions wearing a purchasing costume, so it helps to know which ones to ask first.
Where The Data Actually Goes
The first question is not accuracy, it is the data path. Does the tool call a model provider directly, does it store prompts, and what is the retention setting for the plan the business is actually on, not the enterprise tier in the marketing page. A support tool that keeps six months of ticket text is a different risk than one that forwards a redacted payload and keeps nothing.
Ask for the subprocessor list. If the vendor cannot produce one quickly, that answers a lot of other questions at the same time.
Who Reviews The Output
Every AI tool that works in production has a named human in the loop somewhere, even if the marketing says otherwise. Content drafts get an editor, bookkeeping categorization gets a monthly reconciliation, support replies get a queue owner who watches the escalations.
When nobody owns review, the tool does not fail loudly. It fails quietly for weeks and then someone finds a pile of wrong invoices. The review step is the whole difference between a tool that saves time and one that moves work downstream.
What The Real Cost Looks Like
Seat pricing hides usage pricing. A content tool at 30 dollars a seat is fine until the team generates images, and a support bot priced per resolution gets expensive exactly when it is working. The number worth estimating before signing is cost per completed task, at the volume the business will hit in month three.
I keep this breakdown of AI tools by category around for the buyer side of this conversation, since it lays out the categories, realistic cost ranges, and how to evaluate one without buying the whole stack first.
The Short Version
Ask where the data goes, ask who reviews the output, and estimate cost per completed task instead of per seat. Those three answers predict whether a tool is still in use next year better than any demo does.
