AI in graduate recruitment
The problem
Screening hundreds of thousands of early-career applicants manually is slow, expensive and inconsistent.
The AI approach
Candidates complete neuroscience-based games (Pymetrics) and recorded video interviews assessed by AI (HireVue), narrowing the pool before human interviews.
Evidence it works
Screening ~250,000 applications for ~800 early-career roles across 50+ countries, Unilever reported saving ~50,000 hours of interview time over 18 months and £1M+ a year in costs, cutting time-to-hire from roughly four months to four weeks (~90%), lifting candidate completion to ~96%, and increasing diversity of hires by ~16%. The approach also evolved: HireVue dropped facial-expression analysis in 2021 — after a US FTC complaint over bias and validity — moving to assess language and content instead.
What “good” looks like
Faster, more consistent early screening that widens access and reduces bias — with transparent, defensible assessment criteria.
Feasibility & cost shape
Vendor platforms make deployment feasible, but the governance, fairness testing and candidate-communications work is the substance.
Useful for both the efficiency gain and the lesson that explainability isn’t optional in high-stakes, regulated decisions about people. We’d lead with the governance design.
Based on publicly reported information about the Unilever (with HireVue and Pymetrics) work.
This is an industry example included for illustration. It is not a Leia Intelligence project, and no client of ours is implied. Figures are as publicly reported by the original parties.
Sources: Unilever / HireVue / Pymetrics case reporting · coverage of HireVue's 2021 move away from facial analysis