Mechanical Engineering Technicians

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Mechanical Engineering Technicians (ISCO-08 3115) score an average of 0.26 on a 0–1 exposure scale — more exposed than about 48% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Preparing detailed estimates of quantities and costs of materials and labour required for manufacture and installation according to the specifications given;”

Scores 0.41 on the 2025 scale. The task of preparing detailed estimates of quantities and costs of materials and labour for manufacture and installation involves several elements that can be supported by Generative AI, such as data analysis, calculations, and preliminary report generation. Similar tasks in the context, like "Calculating material requirements based on projects" (0.25) or "Determining material and time standards" (0.4775), show potential for AI assistance in the computational aspects of the task. However, the necessity for human oversight in understanding project-specific conditions, interpreting detailed specifications, and ensuring accuracy in the estimates is significant. These elements limit the potential for full automation, as seen in tasks involving domain-specific expertise and judgment like "Reporting construction works for acceptance" (0.31). Given the prevalent digital infrastructure in Poland, AI can significantly enhance efficiency in generating estimates but cannot entirely replace the nuanced human insight required. Therefore, aligning the score closer to tasks with higher automation potential in standard calculations, yet accounting for human judgment, a score of 0.48 is justified.

Moving fastest, 2023 → 2025

“Assembling and installing new and modified mechanical assemblies, components, machine tools and controls, and hydraulic power systems;”

Model capability on this task changed by +0.04 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 3115, 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.

• Engineering Technologists and Technicians, Except Drafters, All Other
• Non-Destructive Testing Specialists
• Photonics Technicians
• Mechanical Engineering Technologists and Technicians
• Automotive Engineering Technicians
• Electro-Mechanical and Mechatronics Technologists and Technicians
• Robotics Technicians
• Aerospace Engineering and Operations Technologists and Technicians

In context

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

Mechanical Engineering Technicians sit at the 48th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mechanical Engineering Technicians rank in the 48th 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 0.07 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Preparing detailed estimates of quantities and costs of materials and labour required for manufacture and installation according to the specifications given;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mechanical Engineering Technicians". https://singulariki.com/gradient/3115-mechanical-engineering-technicians.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.