Civil Engineering Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Civil Engineering Labourers (ISCO-08 9312) score an average of 0.09 on a 0–1 exposure scale — more exposed than about 2% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Cleaning worksites and removing obstructions.”

Scores 0.13 on the 2025 scale. The task "Cleaning worksites and removing obstructions" involves significant physical labor similar to other tasks in the provided dataset, such as "Sweeping floors and surfaces, washing stairs, windows..." (0.075), "Cleaning and maintaining the spindle polishing workshop and removing minor damages" (0.1125), and "Cleaning the construction site and removing various obstacles and blockages" (0.115). These tasks require manual dexterity, human intervention, and a tangible presence, which Generative AI lacks the capacity to perform directly. While AI can assist with planning and logistics, such as scheduling or providing procedural guidelines, the core activities require direct physical participation. The adjusted score reflects the task's predominantly manual nature and aligns with the scores of similar tasks that also involve physical cleaning and maintenance duties. Despite the benefits of high technological access in a high-income country like Poland, the essential manual aspects of this task remain non-automatable by current AI technologies.

Moving fastest, 2023 → 2025

“Shovelling and spreading gravel and related materials;”

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 9312, 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.

• Highway Maintenance Workers
• Helpers, Construction Trades, All Other
• Rail-Track Laying and Maintenance Equipment Operators

In context

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

Civil Engineering Labourers sit at the 2nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Civil Engineering Labourers rank in the 2nd 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 worksites and removing obstructions.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Civil Engineering Labourers". https://singulariki.com/gradient/9312-civil-engineering-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.