Is your AI sorting good enough? Look beyond the accuracy score

Ask which mistakes the score hides

Before letting an assistant sort incoming work, ask what happens to the requests it gets wrong. Google explains that overall accuracy can be misleading when one category is much less common than another. A useful review also separates missed matches from incorrect alerts. For a business owner, that means asking both whether important requests were found and whether the resulting work queue was useful.

Defines classification measures and explains why overall accuracy may conceal important errors. The delivery-change calculation is hypothetical. Google: accuracy, precision and recall

A worked example with real consequences

Consider a hypothetical test of 200 customer messages containing eight delivery-change requests. The assistant flags ten messages: six are genuine changes and four are unrelated. It misses two changes. That is 194 correct classifications out of 200, or 97% accuracy. Yet it found only six of eight changes, or 75% recall, and only six of ten alerts were useful, or 60% precision. These are illustrative counts, not Exovara results. The missed delivery changes deserve attention even though the overall score looks impressive.

Agree what the label means before testing

For this proposed delivery-change workflow, have a staff member distinguish an actual new instruction from a question about whether a change is possible. Decide how to handle a message referring to two orders, a change already confirmed by phone or a forwarded conversation containing outdated instructions. Record the expected label beside the original test message and the reason for it. If two colleagues disagree, resolve the operating rule before treating either answer as proof of an AI mistake.

Create a review sheet the team can use

Use columns for message reference, staff decision, suggested label, missing context and review outcome. Include a count of messages the assistant could not process. Do not quietly omit these from the report. Keep customer data inside the access arrangements approved for the project. Trial the labels without changing deliveries, then have staff inspect both the flagged queue and messages that were left out. Reviewing only flagged items cannot reveal changes the assistant missed.

Choose the tradeoff deliberately

Google's threshold guidance explains that changing the cutoff for a classifier can change the balance between false alerts and missed cases. There is no universally correct cutoff. For this example, decide how much extra review the dispatch team can handle and which missed instructions would make the process unacceptable. Ask whether the chosen tool actually exposes a meaningful adjustable score. A language model writing a confidence percentage is not, by itself, evidence that the percentage predicts correctness.

Explains how decision thresholds affect classification outcomes. The business review process is proposed implementation guidance. Google: classification thresholds

Separate a rehearsal from a performance claim

Keep difficult examples for checking agreed behaviour, but do not describe an intentionally assembled challenge set as a typical week's workload. After adjusting the workflow, evaluate fresh examples that were not used to make those adjustments. Report the raw counts and the sample period alongside percentages. A small successful test supports a limited next step; it does not establish that future messages will behave the same way. Continue checking ordinary messages and exceptions after launch.

Decide whether the queue earns its place

Measure time spent sorting manually, reviewing suggestions and correcting missed requests. Compare that with setup, software and ongoing monitoring costs. Faster classification is useful only if the resulting handover helps the team finish work. Start with one narrowly defined label, retain staff approval for delivery changes and agree who can pause the assistant. Exovara can help define the review sheet and assess results against your own workload before expanding the system.

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This guide applies across Canada. We provide remote AI consulting for businesses in Charlottetown; it does not describe a local client or a staffed office.

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