How to Evaluate an AI Tool for Your Business

A convincing AI tool demo is easy to find. A good fit for your business is harder, because the demo was built on someone else's data, someone else's workflow and a best-case example. The way to close that gap is a short, structured evaluation you run yourself. This guide walks through it in order, from the problem to the contract, using a tool that reads vendor invoices as the running example.

Start with the problem, not the tool

Write down, in two or three sentences, what goes wrong today and how you would know it improved. "We want to use AI" is not a problem. "Keying vendor invoices into our accounting system takes a large part of one person's week, and typos surface at month-end close" is.

Then test the fit:

• Frequency. Does the task happen often enough that an improvement matters?
• Input. Is the material something a tool can read, such as digital documents, clean scans or structured data?
• Checkability. Can someone tell whether an output is right? If nobody can, you cannot evaluate the tool, and you should not deploy it.
• Cost of error. What happens when it is wrong? A clumsy draft is a nuisance. A wrong payment amount is a loss.

If a simpler fix exists, such as a template, a validation rule, or a feature in software you already pay for, try that first. It sets a floor the AI tool has to beat.

Measure the baseline before the pilot

A pilot with no measured "before" cannot tell you whether anything improved. Pick two to four metrics tied to the problem and record them for the current process over a normal stretch of work:

• Time per item, using the typical case rather than the best case.
• Error rate, found by checking a sample against the source documents.
• Backlog or throughput.
• Cost per item, including staff time.

Write down the result that would justify switching before you see any pilot data. Otherwise almost any result can be read as success.

Run a small, bounded pilot

Keep the pilot narrow: one team, one workflow, a fixed period, and real work rather than showcase examples. During the pilot:

• Keep a person checking every output.
• Log every failure along with the input that caused it.
• Measure the same metrics as the baseline, the same way.
• Track time spent fixing outputs, not just time saved producing them. A tool that is fast but needs heavy correction can be slower overall.
• Ask the people doing the work what they had to fix and what they stopped trusting.

Test accuracy on your own data

A vendor's accuracy figure was measured on the vendor's test set, not your documents. Build your own test set, sometimes called a gold set:

1. Collect a few dozen to a few hundred real examples, more if errors are expensive.
2. Include the awkward cases on purpose: poor scans, unusual layouts, handwritten notes, credit memos, multi-page invoices.
3. Record the correct answer for every field before you run the tool.
4. Score field by field, not document by document. "Invoice correct" hides which fields fail.
5. Count plausible errors separately. A total that is wrong but looks reasonable is more dangerous than an obvious failure, because a reviewer will pass it.

For invoices, a tool that occasionally swaps the invoice date and the due date will look accurate at a glance and still cause late payments. Only field-level scoring on your own documents catches that. Rerun the same gold set whenever the vendor changes the model behind the product or you change settings, because outputs can shift between versions.

Ask the privacy and security questions in writing

Get answers in writing, ideally in the contract, not from a sales call:

• Is our data used to train or improve models? What is the default, and can we opt out contractually?
• How long are inputs and outputs retained, where are they stored, and can we delete them on request?
• Which subprocessors, including any outside model provider, receive our data?
• Is there an independent audit? A SOC 2 report, in the AICPA's description, covers an examination of controls at a service organization relevant to security, availability, processing integrity, confidentiality or privacy. Ask for the report itself, not a badge. Check which of those categories it covers, and whether it is a type 2 report, which also tests how the controls operated over a period of time rather than only how they were designed.
• How are access and logging handled: single sign-on, roles, audit logs?

If the tool reads documents, emails or web pages, ask how it resists prompt injection, where instructions hidden in the content try to steer the model. The OWASP Top 10 for LLM Applications 2025 lists prompt injection first (LLM01) and sensitive information disclosure second (LLM02), and includes excessive agency (LLM06) for systems given more power to act than they need. Ask exactly what the tool can do on its own: read, draft, or also send, pay and delete.

Count the total cost, not the sticker price

The subscription is often not the largest line. A full estimate includes:

• Seat fees plus any usage-based charges (per page, per task, per token) and overage rates.
• Setup and integration time.
• Review time, since a person stays in the loop.
• Rework and the cost of errors that slip through.
• Training and the time it takes the team to change habits.
• The cost of switching away later.

Convert it to cost per completed, checked item and compare that with the baseline. Then rerun the math at several times your pilot volume, because usage pricing that looks small in a pilot can grow quickly.

Plan the exit before you sign

Lock-in is cheapest to handle before the contract, not after. Ask:

• Can we export our data, settings, templates and outputs in a standard format?
• Does the product depend on one model provider, and what happens to us if that provider changes prices or retires a model?
• How much notice do we get for price changes, and is data deleted at termination?

Keep your baseline numbers and gold set in your own files. They are what let you evaluate a replacement in days instead of months.

Use the NIST AI RMF as the frame

The NIST AI Risk Management Framework was released on January 26, 2023, for voluntary use. NIST added a Generative AI Profile (NIST AI 600-1) on July 26, 2024, and as of September 2026 says the AI RMF 1.0 is being revised as part of the White House AI Action Plan. Its core has four functions, and NIST describes govern as cross-cutting, with the process meant to be iterative rather than a one-time sequence.

The evaluation above maps onto those functions:

The framework also lists characteristics of trustworthy AI, including valid and reliable, secure and resilient, and privacy-enhanced. A small business does not need a compliance program to use it. It needs each row of that table to have a named owner and a written answer.

The single most useful artifact from all of this is the gold set. It takes some hours to build, and it lets you retest the tool every time the vendor changes what runs underneath it.