Beauticians and Related Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Beauticians and Related Workers (ISCO-08 5142) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 28% 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 Not exposed 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Arranging appointments and collecting payments.”

Scores 0.54 on the 2025 scale. The task of arranging appointments and collecting payments shares similarities with other service and sales tasks, particularly those involving routine scheduling and financial transactions, like "Preparing invoices and receipts as well as accepting payment for services rendered" (adjusted score: 0.45) and "Accepting payments for sold products" (adjusted score: 0.375). Generative AI can automate aspects of this task by handling scheduling (calendars, reminders) and processing payments (automating digital transactions, generating receipts). However, human oversight remains important for handling exceptions, ensuring accuracy, and managing customer interactions, especially for more nuanced negotiations or adjustments. The task also aligns with the capabilities of digital payment systems and online scheduling tools, given the technological access in a country like Poland. Therefore, the proposed score reflects the substantial potential for automation in these procedural elements while considering the necessary human intervention for exceptions and complexities.

Moving fastest, 2023 → 2025

“Attending to clients taking baths and administering elementary massage;”

Model capability on this task changed by +0.03 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 5142, 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.

• Hairdressers, Hairstylists, and Cosmetologists
• Manicurists and Pedicurists
• Skincare Specialists
• Shampooers
• Makeup Artists, Theatrical and Performance

In context

Part of the 5 - Service and sales workers major group. Return to the full gradient to see how the whole group sits.

Beauticians and Related Workers sit at the 28th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Beauticians and Related Workers rank in the 28th 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 fell by 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Arranging appointments and collecting payments.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Beauticians and Related Workers". https://singulariki.com/gradient/5142-beauticians-and-related-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.