Sweepers and Related Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Sweepers and Related Labourers (ISCO-08 9613) score an average of 0.09 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 4 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Cleaning rubbish, leaves and snow from driveways and grounds.”

Scores 0.13 on the 2025 scale. The task of cleaning rubbish, leaves, and snow from driveways and grounds is predominantly a physical activity requiring manual dexterity and real-time decision-making, which Generative AI cannot perform. This task's nature aligns closely with similar tasks from the context provided, such as "Sweeping up trash, cigarette butts, and other waste from streets" (score 0.175) and "Operating self-propelled equipment for street cleaning" (score 0.15), which also require significant physical interaction with the environment. While Generative AI can assist with scheduling or optimizing the routes and efficiency of cleaning operations, the physical execution remains a predominantly manual task. Considering the prevailing access to high-income infrastructure in a country like Poland, AI can augment capabilities but not automate the primary physical tasks. Therefore, the adjusted score reflects the limited potential for automation by AI in this task, taking into account the need for human labor in physically executing the cleaning activities.

Moving fastest, 2023 → 2025

“Cleaning rubbish, leaves and snow from driveways and grounds.”

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 9613, 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.

• Grounds Maintenance 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.

Sweepers and Related Labourers sit at the 3rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Sweepers and Related Labourers 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: "Cleaning rubbish, leaves and snow from driveways and grounds.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Sweepers and Related Labourers". https://singulariki.com/gradient/9613-sweepers-and-related-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.