Building Frame and Related Trades Workers Not Elsewhere Classified

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 3 task statements that define Building Frame and Related Trades Workers Not Elsewhere Classified (ISCO-08 7119) 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 3 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Demolishing buildings and other structures.”

Scores 0.10 on the 2025 scale. The task of demolishing buildings and structures predominantly involves physical labor, requiring manual dexterity, the use of machinery or tools, and on-site human judgment. Generative AI, such as ChatGPT, cannot perform physical tasks or replace skilled hands-on work. Similar physically demanding tasks, like dismantling stone elements (score: 0.0695), loading and unloading materials (score: 0.0955), and securing excavation edges (score: 0.05), were scored low on automation potential due to their reliance on human physical presence and decision-making. These tasks highlight the limitations of AI in automating highly manual activities. While AI could assist in planning or safety simulations, it cannot execute the core physical activities of demolition. Thus, considering the context of high technological access in a country like Poland, the adjusted score acknowledges the predominantly manual nature of this task and aligns with the low automation potential demonstrated by similar tasks.

Moving fastest, 2023 → 2025

“Climbing and performing miscellaneous construction and building maintenance work on tall structures such as towers, chimneys and spires;”

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 7119, 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.

• Hazardous Materials Removal Workers
• Solar Photovoltaic Installers
• Fence Erectors
• Manufactured Building and Mobile Home Installers
• Construction and Related Workers, All Other
• Weatherization Installers and 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.

Building Frame and Related Trades Workers Not Elsewhere Classified sit at the 2nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Building Frame and Related Trades Workers Not Elsewhere Classified 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 rose by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Demolishing buildings and other structures.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Building Frame and Related Trades Workers Not Elsewhere Classified". https://singulariki.com/gradient/7119-building-frame-and-related-trades-workers-not-elsewhere-classified.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.