Electrical Engineering Technicians

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Electrical Engineering Technicians (ISCO-08 3113) score an average of 0.27 on a 0–1 exposure scale — more exposed than about 50% 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

“Providing technical assistance in research on and development of electrical equipment and facilities, or testing prototypes;”

Scores 0.31 on the 2025 scale. The task of providing technical assistance in research on and development of electrical equipment and facilities, or testing prototypes, involves complex technical knowledge, hands-on skills, diagnostic capabilities, and iterative testing processes. Generative AI can assist with some tasks, such as data processing, suggesting improvements based on patterns, and even generating initial drafts of technical specifications or reports. However, the physical setup, hands-on testing, and precise adjustments required for developing or testing prototypes cannot be fully automated with AI. Several semantically similar tasks shared in the provided context, such as "Conducting measurements and testing subassemblies" (0.3) and "Diagnosing the technical efficiency of technological devices" (0.3), indicate a moderate automation potential, mainly due to their manual and interpretative nature. Additionally, the practical aspects of providing assistance in research and development or testing prototypes require a blend of technical understanding and manual skills, aligning it more closely with tasks like "Ensuring proper use of diagnostic tools" (0.275) or "Checking and maintaining technical equipment" (0.27), where AI can serve a supportive role but not replace the need for human expertise. Considering these comparisons and the task's context, your proposed adjusted score of 0.275 reflects the partial but limited role AI can play, supporting human workers in enhancing productivity by taking over repetitive, data-driven, or routine elements of the task while maintaining the necessity of skilled human involvement.

Moving fastest, 2023 → 2025

“Designing and preparing blueprints of electrical installations and circuitry according to the specifications given;”

Model capability on this task changed by +0.09 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 3113, 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.

• Electrical and Electronic Engineering Technologists and Technicians
• Electro-Mechanical and Mechatronics Technologists and Technicians
• Robotics 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.

Electrical Engineering Technicians sit at the 50th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Electrical Engineering Technicians rank in the 50th 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Providing technical assistance in research on and development of electrical equipment and facilities, or testing prototypes;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Electrical Engineering Technicians". https://singulariki.com/gradient/3113-electrical-engineering-technicians.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.