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Upwage x PRN: AI interviewing cuts turnover in half and saves $1.4M
$1.4M annual turnover savings
AI skeptic to AI advocate
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Why it works
The skeptic-to-advocate arc gives the piece a human spine that pure metrics-first stories lack. The mechanism is unusually specific: Brandon tuned the AI interviewer with performance review data from existing top performers, which explains the quality lift rather than just asserting it. The story sits at the boundary of Efficiency and Breakthrough because a number went down (turnover, hire time) while recruiter capacity tripled upward; the headline leads with savings and turnover reduction, so Efficiency wins the tie-break.
Steal this
Tune the AI with real performance data from top incumbents and name that tuning step explicitly. It transforms a vague 'AI improves quality' claim into a replicable mechanism readers can interrogate.
Click to enlarge ↗ This is editorial commentary and curation. The case study, screenshot, and all metrics are Upwage's published work; we link to the source and lead with our analysis.