Casting Directors

Arts, Design, Entertainment & Media

AI exposure

  • Data source: BLSPublished: 2026-08

    High· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.092

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.37

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 53% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 2 hidden tasks

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Prepare actors for auditions by providing scripts and information about roles and casting requirements.

O*NET Task ID 11020

1.0
Review performer information, such as photos, resumes, voice tapes, videos, and union membership, to decide whom to audition for parts.

O*NET Task ID 11015

0.5
Read scripts and confer with producers to determine the types and numbers of performers required for a given production.

O*NET Task ID 11016

0.5
Select performers for roles or submit lists of suitable performers to producers or directors for final selection.

O*NET Task ID 11017

0.5
Audition and interview performers to match their attributes to specific roles or to increase the pool of available acting talent.

O*NET Task ID 11018

0.5
Maintain talent files that include information such as performers' specialties, past performances, and availability.

O*NET Task ID 11019

0.5
Serve as liaisons between directors, actors, and agents.

O*NET Task ID 11021

0.5
Attend or view productions to maintain knowledge of available actors.

O*NET Task ID 11022

0.5
Negotiate contract agreements with performers, with agents, or between performers and agents or production companies.

O*NET Task ID 11023

0.5
Contact agents and actors to provide notification of audition and performance opportunities and to set up audition times.

O*NET Task ID 11024

0.5

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information