Summary of "Unilever case Study | HR case Study | Unilever Recruitment | AI in HR | Artificial Intelligence"

Overview

Case study of Unilever’s experiment using AI-driven assessments to scale hiring for its Future Leaders program (~50 countries).
Pilot combined game-based assessments (pemetrics/pemetrics) and on-demand video-interview AI (higher view/HireVue) to automate screening and predict job performance.
Business goals: attract millennial talent (target cited: 60% of workforce millennials by 2020), speed hiring, reduce costs, and improve diversity and candidate experience.

Replace paper/phone/manual screening with a digital funnel:

Online application
Game-based cognitive & behavioral assessment (pemetrics) to measure traits such as aptitude, reasoning, risk appetite, and motivational drive
On-demand asynchronous video interview (higher view) — candidates record responses
AI analysis: extract features from video and games, produce an insight/ranking score
Shortlist candidates for assessment centers where human recruiters conduct final selection

Combined human + AI workflow: automated pre-screening at scale, human-led assessment centers for final hiring decisions.
Pilot → measure → scale approach across a global leadership intake.

Digital hiring funnel / hybrid automation playbook (AI screening + human confirmation)
Game-based assessment as behavioral/psychometric measurement (adaptive, progressive)
Asynchronous video interviewing + ML scoring (cited: ~25,000 signal features)
Objective-data-first selection to reduce subjective bias (resume/interpreter bias reduction)
Pilot → measure → scale for iterative rollout

Time / cost

Saved ~50,000 candidate interview hours over 18 months
Reported ~£1 million annual cost savings
Historically took 4–6 months to screen 250,000 applications; AI reduced screening time (specific baseline not fully quantified)
Reported 90% reduction in time-to-hire

Candidate engagement / quality

AI scoring / features

Example insight score: 65% for one candidate
Feature set: ~25,000 features analyzed (facial expressions, eye contact, body movement, voice nuances, clothing details)

Media demo: Business Insider reporter Rich Filoni (Rich Feloni in some citations) took the on-demand interview, received a 65% insight score and was ranked second in that pool.
Outcome for Unilever’s Future Leaders program: reduced applicant pool from 250k → 3.5k invited to assessment centers → hired 800; reported savings and diversity gains as above.

Example insight: one candidate was given a 65% “insight score” and ranked second in their pool.

Candidate benefits

More engaging assessments via games versus traditional psychometrics
Flexibility through asynchronous interviews (time-zone coverage)
Immediate feedback from game levels; potentially less stress and stereotype-threat
Perceived reduction in human bias due to objective data

Client benefits

Use game-based assessments to measure cognitive and behavioral traits that resumes miss; ensure tests are adaptive and emphasize process over “right answers.”
Deploy asynchronous video interviewing for scale and global time-zone coverage; analyze video with ML to surface nonverbal and vocal predictors.
Combine AI pre-screening with human-led assessment centers for final hires to retain human judgment for high-stakes decisions.
Pilot with a targeted program (e.g., leadership intake), measure completion rates, time-to-hire, cost per hire, and diversity outcomes before scaling.
Track and publish metrics: candidate completion rate, time-to-hire reduction, hours saved, cost savings, diversity lift, and predictive validity against job performance.
Validate ML models against actual performance outcomes to avoid false positives and ensure fairness.

AI recruiting is “still in its infancy” — requires careful validation and continuous monitoring for bias and adverse impact.
Transparency: insights and scoring should be explainable to candidates and regulators.
Privacy and consent: recording and analyzing video and behavioral games raises data-protection requirements (e.g., GDPR).
Human oversight remains important to catch model errors and preserve candidate experience.

Harry — speech robot narrator used in the video
Rich Filoni (Rich Feloni in many citations) — senior strategy reporter, Business Insider (appears in demo)
Lauren Larson — cited as Chief Technology Officer at “higher view” in the video
Vendors referenced: “pemetrics” (game-based assessment) and “higher view” (video interviewing)
- Note: subtitles contain possible misspellings; likely referring to Pymetrics (game-based) and HireVue (video interviewing)
Video creator/channel: 5 Minutes Learning (YouTube)