Structural Metal Preparers and Erectors

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Structural Metal Preparers and Erectors (ISCO-08 7214) score an average of 0.11 on a 0–1 exposure scale — more exposed than about 5% 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

“Assembling and erecting the framework and other metal parts of ships' structures;”

Scores 0.14 on the 2025 scale. The task of assembling and erecting the framework and other metal parts of ships' structures is highly manual and requires significant physical dexterity, careful alignment, and safety considerations that current Generative AI cannot automate. This task is similar to other craft and trade tasks such as "Performing and repairing elements of aluminum, steel, and brass plates of ship equipment" and "Assembling power supply, control, and regulation systems of new lifting devices," which score relatively low due to their manual nature. AI might assist in instructional guidance or planning sequences but cannot perform the physical execution. Given these similarities and the constraints current AI technology presents concerning manual tasks, a score of 0.12 represents the limited potential for automation, even with AI's possible role in supporting non-physical aspects. Furthermore, the context assumes performance in a high-income country like Poland, which offers technological support but not for full task automation.

Moving fastest, 2023 → 2025

“Assembling and erecting the framework and other metal parts of ships' structures;”

Model capability on this task changed by +0.04 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 7214, 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.

• Structural Iron and Steel Workers
• Structural Metal Fabricators and Fitters
• Reinforcing Iron and Rebar Workers

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.

Structural Metal Preparers and Erectors sit at the 5th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Structural Metal Preparers and Erectors rank in the 5th 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: "Assembling and erecting the framework and other metal parts of ships' structures;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Structural Metal Preparers and Erectors". https://singulariki.com/gradient/7214-structural-metal-preparers-and-erectors.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.