How to Evaluate an AI Tool Before Your Team Adopts It

Every AI tool demo is impressive, because demos are chosen to be. The question that matters is narrower: will this tool do our work better, safely, at a cost that makes sense? A structured evaluation answers that in a few weeks and protects you from paying for software nobody uses after the first month.

Step 1: Define the job, not the tool

Write one sentence describing the work you want help with and how you do it today. "Our two support staff spend about ten hours a week writing first replies to routine customer questions" is a job. "We need an AI chatbot" is a purchase looking for a reason.

Then write down how you will judge success: time saved, error rate, customer satisfaction, or turnaround time. If you cannot measure the current situation, measure it for a week before you start.

Step 2: Build a test set from real work

Collect 20 to 30 genuine examples of the task, with the good answers you would expect. Include awkward cases: ambiguous requests, missing information, the questions that trip up new staff. Remove or anonymise personal data first.

Run every tool you are considering against the same test set. Score each output simply: usable as is, usable with light edits, or not usable. This one exercise usually separates contenders more clearly than any feature comparison.

Step 3: Review data handling

Ask every vendor the same questions and get the answers in writing:

• Is our input or output used to train models? Can that be switched off contractually?
• Where is data stored and processed, and for how long is it retained?
• Who at the vendor can access our data, and under what circumstances?
• Does the product support single sign-on, user roles and audit logs?
• What security certifications or independent audits can you share?
• How do we export or delete our data if we leave?

Compare the answers with your AI use policy. If the tool cannot meet your data rules, the evaluation ends here, however good the demo was.

Step 4: Check accuracy and failure modes

Look beyond the average result:

• What happens when the tool does not know? Does it say so, or invent an answer?
• Does it cite sources, and are those sources correct?
• How consistent is it if you ask the same thing twice?
• Can a user easily correct or override it?

For background on why confident errors happen, see AI hallucinations and how to check AI output.

Step 5: Work out the real cost

Licence price is only part of it. Include setup time, training, integration with existing systems, the review time AI output still needs, and usage-based charges that grow with volume. A tool that saves ten hours a week but needs eight hours of checking is not saving much.

Step 6: Run a time-boxed pilot

1. Choose three to five people who do the task regularly, including at least one sceptic.
2. Run the pilot for two to four weeks on real work.
3. Track the success measure you defined in step 1.
4. Hold a short review at the end: what worked, what did not, and what would need to change.

Step 7: Decide, and set a review date

Make a clear decision: adopt, adopt with changes, or stop. If you adopt, write down the conditions (approved uses, data rules, who reviews output) and schedule a review in three to six months. AI products change quickly, both in capability and in terms of service.

A scorecard you can copy

Treat "data handling" as pass or fail before you score anything else.