Before You Adopt an AI Tool, Test It on Your Actual Work

Define a task, not an ambition

“Use AI in the business” is too broad to evaluate. “Draft a reply to a delivery-status enquiry using only the supplied order record” gives you a task, a boundary and a way to judge the output. Begin with a reversible workflow where a person can inspect the result before it reaches a customer.

This guide proposes an evaluation method. It does not endorse a vendor or report a product test conducted by ITBank. Google’s machine-learning guidance distinguishes model-quality measures from business outcomes: a model can look better on one metric without making the service more useful.

Build a representative trial set

Create an illustrative set of 20 enquiries using invented customer details. Include ordinary cases, missing information, ambiguous wording, incorrect assumptions and requests outside the tool’s scope. Include the languages your audience actually uses, with a competent speaker reviewing each relevant language.

For an African business, avoid treating English output as evidence that local-language support works. Test the terms, currency formats and place names your customers encounter. These are test-design considerations, not a claim about any provider’s language accuracy.

Decide what a correct response means

For each enquiry, write down the facts the reply must retain and the actions it must avoid. An order without a dispatch date should not acquire an invented delivery promise. A refund question should not trigger an unauthorized refund.

Keep a simple record with input, expected facts, model output, error type and reviewer decision. Compare against your existing process. If your current template solves the task reliably, the new tool needs to justify its additional cost and complexity.

Separate different failure types

Record factual errors separately from tone problems. A polite reply containing an invented payment instruction is not a minor wording issue. Also record omissions, unsupported claims, inappropriate disclosure and unnecessary refusals. Decide which failures block use before viewing the results.

Run the same cases again after changing instructions or switching models. Retain a separate set of fresh cases so that repeated tuning does not merely teach the workflow your demonstration examples.

Measure the complete workflow

An inexpensive generated reply may become expensive after correction. For a hypothetical trial, 20 replies at two minutes each for review require 40 minutes of staff attention. If manual replies take one minute each, the tool has not saved time in that trial, regardless of generation speed.

Count successful completed tasks, review time, rework and usage charges. Keep currencies explicit. Provider prices, taxes and data practices need to be checked separately for your account and jurisdiction.

Use a controlled rollout

Start with internal drafts and a named reviewer. Define a fallback when the model cannot answer. Do not submit confidential customer information until the organization has checked the provider’s data terms and established an appropriate process.

After a limited trial, write a decision: adopt for a narrow task, revise the workflow or stop. A documented decision not to automate is also a useful result.

Sources and further reading

Google: Measuring success in ML projects

Google: Generative AI evaluation terminology