Civil Engineering Technicians

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Civil Engineering Technicians (ISCO-08 3112) score an average of 0.28 on a 0–1 exposure scale — more exposed than about 51% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Assisting with the preparation of detailed estimates of quantities and costs of materials and labour required for projects, according to the specifications given;”

Scores 0.41 on the 2025 scale. The task of assisting with the preparation of detailed estimates of quantities and costs of materials and labour required for projects involves a combination of interpretive skills, quantitative analysis, and technical knowledge. Generative AI can significantly aid in data processing, calculations, and even preliminary document preparation, much like the task of "Calculating material requirements based on the project presented in the drawing and technological documentation" with an adjusted score of 0.25. However, the need for human oversight in interpreting specifications, adapting to project-specific conditions, and ensuring compliance with local regulations is crucial. Tasks such as "Determining material and time standards as well as price calculation for specified finished products" and "Preparing technical documentation of the work performed as well as tender documentation" with adjusted scores of 0.4775 and 0.515 respectively, show that while AI can automate routine aspects, human expertise is essential for comprehensive understanding and decision-making. Considering these factors and the task's setting in a high-income country like Poland, an adjusted score of 0.47 reflects the substantial but not complete potential for automation, balancing AI's capability in routine data handling with the need for human oversight in nuanced interpretation and final estimation.

Moving fastest, 2023 → 2025

“Performing or assisting with field and laboratory tests of soils and construction materials;”

Model capability on this task changed by +0.10 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 3112, 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 and Building Inspectors
• Energy Auditors
• First-Line Supervisors of Firefighting and Prevention Workers
• Civil Engineering Technologists and Technicians
• Surveying and Mapping Technicians
• Fire Inspectors and Investigators

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Civil Engineering Technicians sit at the 51st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Civil Engineering Technicians rank in the 51st 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Assisting with the preparation of detailed estimates of quantities and costs of materials and labour required for projects, according to the specifications given;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Civil Engineering Technicians". https://singulariki.com/gradient/3112-civil-engineering-technicians.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.