Roofers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Roofers (ISCO-08 7121) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 9% 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

“Studying drawings, specifications and construction sites to determine materials required;”

Scores 0.28 on the 2025 scale. The task of studying drawings, specifications, and construction sites to determine materials required involves interpreting technical documentation and assessing on-site conditions to make informed decisions about material needs. This task is similar to "Calculating material requirements based on the project presented in the drawing and technological documentation of the product," which had an automation score of 0.25 due to its reliance on interpreting technical data and documents, a process partially within AI's capabilities. However, like "Utilizing technical documentation and performing sketches and drawings of sanitary, heating, and gas installations," which had a score of 0.38, this task requires substantial human expertise for accurate interpretation and on-site judgment. Given that AI can support data analysis and preliminary calculations but lacks the physical and contextual judgment required on construction sites, I estimate a slightly higher adjusted score of 0.28, recognizing the potential of AI in assisting with data interpretation while acknowledging significant human oversight is necessary. The context of the task being in Poland, a high-income country with advanced technology infrastructure, supports the role of AI as an auxiliary tool in automating parts of the process.

Moving fastest, 2023 → 2025

“Studying drawings, specifications and construction sites to determine materials required;”

Model capability on this task changed by +0.08 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 7121, 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.

• Roofers

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.

Roofers sit at the 9th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Roofers rank in the 9th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Studying drawings, specifications and construction sites to determine materials required;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Roofers". https://singulariki.com/gradient/7121-roofers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.