Cleaners and Helpers in Offices, Hotels and Other Establishments

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Cleaners and Helpers in Offices, Hotels and Other Establishments (ISCO-08 9112) score an average of 0.12 on a 0–1 exposure scale — more exposed than about 7% 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 4 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Making beds, cleaning bathrooms, supplying towels, soap and related items;”

Scores 0.14 on the 2025 scale. The task of making beds, cleaning bathrooms, and supplying towels, soap, and related items closely aligns with other semantically similar tasks that involve primarily physical and manual activities, such as "Delivering and replacing bed linen, towels, cleaning supplies, and other room equipment" (automation score of 0.125) and "Replacing room furnishings and sanitary facilities, replenishing storerooms, and distributing bed linen" (automation score of 0.13). These tasks are characterized by their reliance on physical interaction, manual dexterity, and human judgment, which Generative AI cannot automate. While AI might support logistical tasks like inventory management or scheduling, the core physical activities of cleaning and making beds remain manual. Given the high-income context like Poland, where access to technology is prevalent, AI's role might slightly optimize some processes, but the physical nature of the task predominantly limits automation. The adjusted score reflects the minimal automation potential in these largely manual and physical tasks.

Moving fastest, 2023 → 2025

“Making beds, cleaning bathrooms, supplying towels, soap and related items;”

Model capability on this task changed by +0.04 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 9112, 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.

• Janitors and Cleaners, Except Maids and Housekeeping Cleaners
• Maids and Housekeeping Cleaners
• Cleaners of Vehicles and Equipment

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Cleaners and Helpers in Offices, Hotels and Other Establishments sit at the 7th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Cleaners and Helpers in Offices, Hotels and Other Establishments rank in the 7th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Making beds, cleaning bathrooms, supplying towels, soap and related items;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Cleaners and Helpers in Offices, Hotels and Other Establishments". https://singulariki.com/gradient/9112-cleaners-and-helpers-in-offices-hotels-and-other-establishments.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.