Actors

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Actors (ISCO-08 2655) score an average of 0.31 on a 0–1 exposure scale — more exposed than about 59% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Minimal band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Reading scripts and undertaking research to gain understanding of parts, themes and characteristics;”

Scores 0.56 on the 2025 scale. The task of "Reading scripts and undertaking research to gain understanding of parts, themes, and characteristics" involves content analysis, thematic exploration, and creative interpretation, which are areas where Generative AI can provide substantial assistance. AI can process large datasets, summarize content, identify themes, and suggest narratives, making it highly effective for the initial analysis of scripts. However, the task also requires human insight for nuanced understanding, creativity, and emotional interpretation, which AI currently cannot fully replicate. Looking at semantically similar tasks, such as "Careful studying of the screenplay" (0.7) and "Participating in pre-production tasks for film production" (0.465), this task resembles them in combining both technical analysis and creative interpretation. The task may involve deeper structural understanding akin to pre-production analysis but less strictly creative decision-making than screenwriting or concept creation tasks. Considering the context of a high-income country like Poland, where AI tools and digital infrastructure are accessible, the automation potential is higher. Yet, the nuanced understanding necessary for interpreting thematic elements and characteristics prevents full automation. Therefore, this task's adjusted score reflects AI's supportive but not entirely comprehensive role in handling both creative and analytical script-related tasks, aligning closer to pre-production tasks with high reliance on human expertise for completion.

Moving fastest, 2023 → 2025

“Attending auditions and casting calls in order to audition for roles;”

Model capability on this task changed by +0.22 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2655, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Actors

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Actors sit at the 59th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Actors rank in the 59th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Reading scripts and undertaking research to gain understanding of parts, themes and characteristics;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Actors". https://singulariki.com/gradient/2655-actors.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.