AI-assisted job matching for equal value: where it helped and where it embarrassed us

We piloted a language-model tool this year to help group jobs of equal value, at a hotel and leisure group with about 9,000 employees in Spain, Portugal and the UK. We fed it 1,150 job descriptions and asked for a first-pass score on skills, effort, responsibility and working conditions.

Where it helped: it found 210 near-duplicate jobs with different titles in about a day, and its ranking of jobs within a family matched our evaluation panel around 85% of the time.

Where it embarrassed us: it consistently scored housekeeping supervisors below maintenance supervisors on responsibility and effort, because the housekeeping descriptions were shorter and used softer verbs. The descriptions carried the bias and the tool reproduced it faithfully. A panel member caught it; the tool never would have.