Other Cleaning Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Other Cleaning Workers (ISCO-08 9129) score an average of 0.10 on a 0–1 exposure scale — more exposed than about 3% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Applying chemicals and high-pressure cleaning methods to remove micro-organisms from water and filtration systems;”

Scores 0.12 on the 2025 scale. The task of applying chemicals and high-pressure cleaning methods to remove micro-organisms from water and filtration systems is primarily physical and requires real-time decision-making, manual dexterity, and sensory feedback, which are beyond the current capabilities of generative AI to automate. Similar tasks in the cluster, such as "washing and disinfecting" (score 0.15) and "disinfecting workstations" (score 0.12), also reflect the physical nature of such cleaning activities that cannot be automated by AI. While AI might assist with scheduling, generating guidelines, or optimizing the efficiency of chemical use, the core task remains manual. Additionally, the use of AI for support does not extend to the execution of the task. Considering these factors and the higher daily access to technology in Poland, the adjusted score of 0.14 aligns with the understanding of limited automation potential due to the manual labor involved.

Moving fastest, 2023 → 2025

“Applying chemicals and high-pressure cleaning methods to remove micro-organisms from water and filtration systems;”

Model capability on this task changed by +0.02 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 9129, 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.

• Septic Tank Servicers and Sewer Pipe Cleaners
• Building Cleaning Workers, All Other

In context

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

Other Cleaning Workers sit at the 3rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Other Cleaning Workers rank in the 3rd 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Applying chemicals and high-pressure cleaning methods to remove micro-organisms from water and filtration systems;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Other Cleaning Workers". https://singulariki.com/gradient/9129-other-cleaning-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.