House Builders

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define House Builders (ISCO-08 7111) score an average of 0.14 on a 0–1 exposure scale — more exposed than about 13% 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

“Coordinating and supervising the activities of subcontractors, labourers and other workers.”

Scores 0.28 on the 2025 scale. The task of coordinating and supervising the activities of subcontractors, labourers, and other workers involves real-time decision-making, interpersonal communication, and contextual judgment, similar to tasks such as "Supervising other employees" with a score of 0.35 and "Supervising and taking responsibility for the implementation of production tasks" with a score of 0.325. This task shares characteristics with supervisory roles where human oversight is crucial due to the need for situative adjustments and conflict resolution, as reflected in the adjusted scores for tasks like "Supervising and settling commissioned works to external companies" (0.25) and "Directing work on the site and coordinating the progress of construction works" (0.39). Generative AI can assist in scheduling, data analysis, and providing communication support, but it cannot replace the hands-on leadership required for effective supervision. The significant human judgment and adaptability required limit the automation potential, but AI offers valuable support in organizing information and optimizing logistics. Considering Poland's technological landscape, the accessibility of AI tools allows for greater support, positioning the score towards the lower end of the cluster but reflecting a meaningful role for AI assistance. Therefore, the adjusted score of 0.285 accurately captures this balance of automation potential and the necessity of human oversight.

Moving fastest, 2023 → 2025

“Coordinating and supervising the activities of subcontractors, labourers and other workers.”

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 7111, 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.

• Construction Managers

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.

House Builders sit at the 13th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, House Builders rank in the 13th 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: "Coordinating and supervising the activities of subcontractors, labourers and other workers.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "House Builders". https://singulariki.com/gradient/7111-house-builders.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.