Engineering Professionals Not Elsewhere Classified

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Engineering Professionals Not Elsewhere Classified (ISCO-08 2149) score an average of 0.30 on a 0–1 exposure scale — more exposed than about 57% 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

“Designing components of optical instruments such as lenses, microscopes, telescopes, lasers, optical disc systems and other equipment that utilize the properties of light;”

Scores 0.39 on the 2025 scale. Designing components of optical instruments such as lenses, microscopes, and telescopes involves a mix of technical expertise, creativity, and precision, closely resembling tasks such as 'Selecting and preparing optical materials and elements' (0.435) and 'Selecting machines and devices for assembly and repair of optical devices' (0.385). These scores reflect the challenges in automating tasks that require nuanced understanding and physical interaction with materials, especially in precision optics. While Generative AI can assist in design processes, simulations, and material recommendations based on specifications, the actual crafting and intricate design adjustments necessary for optical components still require human expertise. The task involves both routine technical calculations, which AI can support, and creative problem-solving that necessitates human insight. Compared to tasks like 'Grinding glass edges' (0.084642857) or 'Grinding and polishing edges on mirrors' (0.135), the design aspect of optical instruments demands more cognitive input rather than repetitive manual action, offering a higher potential for AI assistance, though not full automation. Considering the high technological access in Poland, facilitating AI support for design and analytical tasks, a score of 0.42 appropriately captures the balance between automation and the continued need for specialized human skills in this field.

Moving fastest, 2023 → 2025

“Designing components of optical instruments such as lenses, microscopes, telescopes, lasers, optical disc systems and other equipment that utilize the properties of light;”

Model capability on this task changed by +0.24 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 2149, 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.

• Engineers, All Other
• Energy Engineers, Except Wind and Solar
• Mechatronics Engineers
• Microsystems Engineers
• Photonics Engineers
• Robotics Engineers
• Nanosystems Engineers
• Wind Energy Engineers

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Engineering Professionals Not Elsewhere Classified sit at the 57th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Engineering Professionals Not Elsewhere Classified rank in the 57th 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 0.08 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Designing components of optical instruments such as lenses, microscopes, telescopes, lasers, optical disc systems and other equipment that utilize the properties of light;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Engineering Professionals Not Elsewhere Classified". https://singulariki.com/gradient/2149-engineering-professionals-not-elsewhere-classified.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.