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What AI-related postings ask for that others do not

The question. Within one discipline, what changes about a job posting when it mentions AI?

What it found

Evaluation language, interpretation and verification all rise. Governance barely moves, and cross-functional briefing does not move at all. The comparison is made inside a single discipline, so both arms pass through the same classifier and a misclassification lands a posting in the wrong discipline while keeping it on the correct side of the AI split.

The numbers

Term groupAIEM elementAINon-AILift
AI oversightDiscernment4.2%0.3%11.82
handing work to a systemDelegation8.3%0.9%7.60
evaluation languageDiscernment42.0%22.9%1.61
interpretationDiscernment30.1%15.4%1.48
ethics, bias, responsible AIDiligence7.9%4.0%1.44
verificationDiscernment36.4%23.9%1.35
experimentation, pilotsTinkering Disposition6.7%5.1%1.25
governanceDiligence11.5%9.7%1.03
cross-functional briefingDescription57.7%58.1%0.96
ownershipInternal Locus of Evaluation19.2%19.0%0.90
ambiguityTolerance for Ambiguity10.2%11.7%0.75

Greenhouse only. Lift is length-adjusted and is therefore smaller than the two percentages divided.

How it was computed

Greenhouse boards only, so the compared groups share a platform, a collection window and an employer pool. Lift is a Mantel-Haenszel estimate pooled over description-length quartiles, because term presence rises with posting length and AI-related postings are longer. lift_unstratified is the plain ratio published before 2026-09-11, kept for provenance only.

How to read it

Lift is adjusted for length, so it is deliberately NOT the ratio of the two percentages beside it. A group appears only where the discipline has 50+ non-AI postings and 5+ postings carry the term in each arm. Only mappings ratified against the AI Engagement Model are shown.

Source view: competency_lift · published 2026-09-11