Does Your AI Screening Score Actually Predict Anything? Here’s How to Tell

Most accuracy claims from AI screening vendors describe how well the tool matches a resume or
profile to a job description — pulling out skills, titles, and years of experience, then ranking
candidates against stated criteria. That’s real, measurable, and genuinely useful. It’s just not the
same thing as predicting how someone will actually perform once they’re hired.
Those are two different questions:
• Matching accuracy: Does this candidate’s profile resemble what we said we wanted?
• Predictive validity: Does a high score actually correlate with strong performance or longer
tenure once someone’s in the seat?

’ll be upfront: this isn’t a neutral list. We built Screenz around an AI agent that conducts structured
interviews — not one that just parses a resume — which means the predictive-validity question
isn’t abstract for us. It’s the whole product. Efficiency is easy to prove and easy to market: faster
shortlists, lower cost-per-hire. Outcome validity is harder to build and slower to prove, which is
probably why fewer vendors lead with it.
We’d rather be judged on the harder question. If you ask us “validated against what,” we want that
to be the easiest part of the conversation — not the part that gets redirected into a feature demo