HR & People
CV screening and shortlisting
Score applications against defined role criteria. Produces a ranked shortlist with rationale. Reduces screening time for high-volume roles.
50–70%
reduction in initial screening time
Use case assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Light data requirements. Uses straightforward inputs (documents, product descriptions, customer records) that are usually accessible with minimal prep.
High risk. Significant external exposure or regulatory implications. Requires robust oversight and clear accountability.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Unilever screens 1.8M annual applications using Pymetrics and HireVue, saving 50,000+ hours of candidate time, £1M annual costs, reducing time-to-hire by 90%, and increasing diversity of hires by 16% (Bernard Marr).
Bias risk is the most significant concern. AI trained on past successful hires can encode historical biases. Audit outputs for demographic patterns before relying on them.
Human review of the AI's shortlist (not just the top candidates) is essential. Document the review process for any future employment tribunal scrutiny.
Define scoring criteria with HR and hiring managers upfront: what matters for this role? AI scores against what you tell it to value.
Validate the scoring criteria against your existing top performers in similar roles before applying them to new candidates. This is the most practical way to check whether the criteria predict success rather than just encode historical preference.
Experiment starter: Take the last completed application cohort for a high-volume role. Define 5–7 scoring criteria with the hiring manager. Score the applications using an LLM and compare the top 20 against who was actually interviewed and offered. If 8 or more of your actual interview candidates appear in the AI's top 20, the criteria are well-calibrated.
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