Earthmoving and Related Plant Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Earthmoving and Related Plant Operators (ISCO-08 8342) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 11% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Operating and monitoring equipment to remove sand, gravel and mud from bottom of body of water;”

Scores 0.15 on the 2025 scale. The task of operating and monitoring equipment to remove sand, gravel, and mud from the bottom of a body of water involves significant physical labor, real-time decision-making, and equipment handling, similar to tasks like "Dewatering the excavation" and "Operating self-propelled equipment for street cleaning and snow removal" with adjusted scores of 0.15 and 0.15, respectively. These tasks require human sensory input, situational awareness, and manual intervention, limiting their automation potential with current Generative AI capabilities. Generative AI can assist in optimization and monitoring through data analysis but cannot perform the physical operations or make on-the-spot decisions needed in dynamic and manual environments. Considering the high-income context like Poland, although AI tools for supporting such tasks might be available, the core activities still rely heavily on human intervention, justifying a score of 0.15.

Moving fastest, 2023 → 2025

“Operating and monitoring excavating machinery equipped with moveable shovel, grab-bucket or dragline bucket, to excavate and move earth, rock, sand, gravel or similar materials;”

Model capability on this task changed by +0.05 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 8342, 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.

• Operating Engineers and Other Construction Equipment Operators
• Paving, Surfacing, and Tamping Equipment Operators
• Pile Driver Operators
• Dredge Operators

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Earthmoving and Related Plant Operators sit at the 11th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Earthmoving and Related Plant Operators rank in the 11th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Operating and monitoring equipment to remove sand, gravel and mud from bottom of body of water;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Earthmoving and Related Plant Operators". https://singulariki.com/gradient/8342-earthmoving-and-related-plant-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.