Test AI features on your own data, on roles you know the answer to. Give the system resumes from candidates you already hired and rejected, and check whether its ranking matches your judgement. Then ask what the feature does when it is wrong, who reviews the output, and whether the behaviour can be explained to a candidate.
Use a known-answer set. Take twenty to fifty resumes from a role you filled recently, including the person you hired, the shortlist and a spread of rejections, and run them through the screening or matching feature without telling it the outcome. Then compare its ranking with your own. This is the closest thing to an objective test available to a buyer, and it takes an afternoon. Look at two things: whether the strong candidates surface near the top, and whether anything obviously unsuitable ranks highly, which usually reveals keyword matching dressed as understanding. Repeat with a second role of a different type, since performance often varies sharply between technical and non-technical hiring. A vendor confident in their feature will support this test rather than steering you toward a curated demo.
Four. What exactly does the feature decide, and is any candidate excluded without a human seeing them. What signal does it use, at least at the level of which fields and attributes it considers, and can a recruiter see why a candidate scored as they did. What happens on failure, meaning how a mis-parsed resume or an unusual career path is handled and how a recruiter corrects it. And where does the processing happen, including whether candidate data reaches a third-party model provider. Vendors vary widely in how precisely they answer these. Precision is itself the signal: a team that can describe what their system does in specific terms usually built it, while vague answers often indicate a feature assembled from a general-purpose model with little evaluation behind it. Compare answers across [the AI-assisted tools you are considering](/ai-recruiting-tools).
They should reorder work rather than remove judgement. Ranking a large applicant pool so a recruiter reads the most relevant applications first is a legitimate use with a clear benefit and a low failure cost, since a mis-ranked candidate is still in the pile. Automatically rejecting candidates below a threshold is a different proposition, with a much higher cost when the model is wrong and additional obligations in some jurisdictions. Decide where your line sits before configuring anything, and write it down. The most common implementation mistake is enabling a feature at its default setting and discovering months later that it was filtering candidates nobody reviewed. Keep a human decision point on anything that removes a person from consideration, and record that the review happened.
Measure it, on a small number of metrics, against the period before you enabled it. Useful measures are recruiter time spent screening per requisition, the proportion of shortlisted candidates who pass the first interview, and hiring manager satisfaction with shortlist quality. Watch the diversity of your shortlists too, since a change in the mix is worth investigating rather than assuming. Give the change a full hiring cycle before judging, and run the comparison on similar roles rather than across your whole pipeline. If the numbers do not move, the feature may still be saving effort, but you should be able to say which effort. Tie the assessment to [the recruiting metrics you already track](/recruitment-metrics) rather than to a vendor-supplied efficiency figure.
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