Agricultural and Industrial Machinery Mechanics and Repairers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Agricultural and Industrial Machinery Mechanics and Repairers (ISCO-08 7233) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 24% 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

“Inspecting and testing new machinery and mechanical equipment for conformity with standards and specifications;”

Scores 0.23 on the 2025 scale. The task of inspecting and testing new machinery and mechanical equipment for conformity with standards and specifications involves a combination of technical expertise, hands-on skills, and physical inspection. In the context provided, similar tasks like the inspection of electrical devices of conveyor belt systems (0.26875), periodic technical inspections of equipment (0.129), and checking the technical condition of machinery (0.135) reflect limited automation potential due to the need for human involvement in detailed and specialized physical assessment and judgment. Generative AI can assist by analyzing data, providing documentation support, and suggesting checklists or compliance points, but cannot fully replace the nuanced human skills required for the physical testing and inspection of new mechanical setups. Given the environment of a high-income country like Poland, where technology can enhance support processes, the score reflects a balanced view of AI's supportive role and the indispensable human expertise needed for the task.

Moving fastest, 2023 → 2025

“Operating newly repaired machinery and equipment to verify the adequacy of repairs;”

Model capability on this task changed by +0.10 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 7233, 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.

• First-Line Supervisors of Mechanics, Installers, and Repairers
• Industrial Machinery Mechanics
• Mobile Heavy Equipment Mechanics, Except Engines
• Maintenance Workers, Machinery
• Millwrights
• Farm Equipment Mechanics and Service Technicians
• Rail Car Repairers
• Wind Turbine Service Technicians

In context

Part of the 7 - Craft and related trades workers major group. Return to the full gradient to see how the whole group sits.

Agricultural and Industrial Machinery Mechanics and Repairers sit at the 24th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Agricultural and Industrial Machinery Mechanics and Repairers rank in the 24th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Inspecting and testing new machinery and mechanical equipment for conformity with standards and specifications;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Agricultural and Industrial Machinery Mechanics and Repairers". https://singulariki.com/gradient/7233-agricultural-and-industrial-machinery-mechanics-and-repairers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.