How to evaluate an AI tool for your business

Start with a task, not a feature list

An AI tool can have an impressive demonstration and still be a poor fit for your business. Before comparing products, describe one thing your team needs to finish: prepare a reviewed RFQ summary, find an approved document, or draft a routine follow-up from verified notes.

Write down who will use the result and what makes it acceptable. If the goal is less administrative work, count review and corrections as part of that work. If the goal is consistency, define which fields or checks must never be skipped.

Also ask whether AI is necessary. A form, template, database query, or conventional integration may be easier to maintain when the task has stable rules. The strongest option is the one that fits the work—not the one with the longest list of AI features.

Establish the data boundary

Before a trial, identify what information the tool would receive. Customer contact details, drawings, pricing, internal procedures, and employee records are not interchangeable categories of data. Get the appropriate internal approval rather than assuming that access to a file means permission to upload it.

Read the current terms for the exact product, plan, and configuration under consideration. Ask how inputs and outputs are retained, who can access them, whether they are used for model training, what deletion options exist, and which third parties process the information. Do not infer business-plan protections from the terms for a consumer account, or vice versa.

NIST's Generative AI Profile includes data privacy and third-party integration risks. This makes vendor review part of the decision, not a detail to leave until after the pilot. Use synthetic or authorized non-sensitive examples while those questions are unresolved.

Test the awkward cases as well as the easy ones

Build a small set of representative tasks with expected answers or review criteria. Include missing information, contradictory documents, revisions, and requests outside the tool's intended scope. Keep the test set separate from examples used to configure the workflow.

Record what happens when the tool does not know. Does it request clarification, provide a source, or produce an answer that looks finished? For a document search task, the correct behavior may be pointing to the approved document or saying it cannot find one—not supplying a plausible substitute.

NIST cautions against drawing broad conclusions from narrow or anecdotal assessments in MEASURE 2.5 of its Generative AI Profile. A successful vendor demo does not establish performance on your own files, exceptions, or operating conditions.

Look beyond the subscription price

The recurring fee is one part of the operating cost. Configuration, integration, data preparation, staff training, review time, and ongoing maintenance also need an owner and a realistic estimate. Check usage limits, additional charges, and the work needed if you later change providers.

Treat prices and product capabilities as current vendor claims until verified for your purchase. This article deliberately does not list subscription prices: they change, and a price without the relevant plan limits can be misleading.

Decide what authority the tool should have

Reading a document, suggesting a response, and sending that response are different permissions. Start with the minimum needed for the pilot. If a tool can change customer records or initiate actions, decide which actions require approval and how errors can be corrected.

Identify who checks outputs, who handles incidents, and who can pause the workflow. Keep the people doing the work involved in that decision. A technically functional tool may still create confusion if nobody understands when to trust it or when to use the manual route.

Make a small decision before a large one

Compare the pilot against the baseline and the acceptance criteria you wrote at the start. Continue only if the complete workflow is better and the remaining risks are understood. A useful decision may be to revise the scope, select a simpler tool, or wait until the data is ready.

The NIST AI Risk Management Framework is a voluntary framework for considering trustworthiness across AI development, use, and evaluation. It is not a product certification. Your buying decision still needs evidence about the particular task, tool, and team involved.