Insulation Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Insulation Workers (ISCO-08 7124) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 10% 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

“Applying insulating materials to exposed surfaces of equipment such as boilers, pipes and tanks;”

Scores 0.15 on the 2025 scale. This score reflects the limited potential for automation in the task of "Applying insulating materials to exposed surfaces of equipment such as boilers, pipes and tanks." The task shares similarities with other manual and physical tasks in the context, such as "Performing thermal insulation of heat-protective tanks, boilers," which received an adjusted score of 0.135 to 0.15. These tasks require manual dexterity, spatial awareness, and problem-solving in potentially unpredictable environments, nuances that Generative AI cannot currently replicate. The physical application of insulation materials cannot be automated by AI, which excels at data processing and content generation but lacks the capability to perform hands-on, tactile tasks. However, AI could potentially assist in scheduling, optimizing material usage, or providing guidelines, but these are peripheral to the main task. Given the manual nature of the job and the capabilities of AI, along with the context of a high-income country like Poland, the automation potential remains low, as indicated by the scores of semantically similar tasks.

Moving fastest, 2023 → 2025

“Applying insulating materials to exposed surfaces of equipment such as boilers, pipes and tanks;”

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 7124, 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.

• Insulation Workers, Floor, Ceiling, and Wall
• Insulation Workers, Mechanical

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.

Insulation Workers sit at the 10th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Insulation Workers rank in the 10th 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Applying insulating materials to exposed surfaces of equipment such as boilers, pipes and tanks;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Insulation Workers". https://singulariki.com/gradient/7124-insulation-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.