Hairdressers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Hairdressers (ISCO-08 5141) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 23% 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 0.60 on the 2025 scale. The task of arranging appointments and collecting payments can be significantly automated by Generative AI, particularly with tools that handle scheduling and payment processing. In the context provided, tasks with similar elements such as "Scheduling sales meetings" had a high potential for automation with scores around 0.775. Additionally, "Issuing sales invoices" had an adjusted score of 0.68, indicating substantial automation potential for tasks involving structured data processes and routine interactions. The automation of appointment arrangements and payment handling can utilize AI to manage schedules, process transactions, and send reminders, fitting well within the higher range of scores for tasks involving digital and structured components. However, human oversight remains necessary for resolving discrepancies and handling complex client interactions, suggesting the task cannot be entirely automated but has high applicability for AI assistance. Given Poland's advanced technological infrastructure, the potential for automating these clerical and transactional components is relatively high, justifying the adjusted score.

Moving fastest, 2023 → 2025

“Cutting, washing, tinting and waving hair;”

Model capability on this task changed by +0.08 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 5141, 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
• Barbers

In context

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

Hairdressers sit at the 23rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Hairdressers rank in the 23rd 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 0.05 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 — "Hairdressers". https://singulariki.com/gradient/5141-hairdressers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.