Electronics Mechanics and Servicers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Electronics Mechanics and Servicers (ISCO-08 7421) score an average of 0.25 on a 0–1 exposure scale — more exposed than about 45% 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

“Keeping records of maintenance and repair work.”

Scores 0.55 on the 2025 scale. The task of keeping records of maintenance and repair work involves data entry, organization, and documentation—areas where Generative AI can significantly contribute by automating repetitive aspects, similar to tasks such as maintaining technological process documentation or a register of contractors, which both have adjusted scores in the 0.44 to 0.70 range. In high-income countries like Poland, with robust technological infrastructure, AI can handle the routine, structured parts of this task, potentially suggesting schedule optimizations or alerting discrepancies detected in data patterns. However, the need for human oversight to ensure accuracy and contextual appropriateness limits full automation. Tasks involving pure documentation and routine data management generally score higher in automation potential within this context, reflecting the role of AI in streamlining processes but recognizing the necessity for nuanced human input in complex scenarios. Hence, an adjusted score of 0.45 accounts for these dynamics effectively.

Moving fastest, 2023 → 2025

“Installing electronic instruments and control systems;”

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 7421, 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.

• First-Line Supervisors of Mechanics, Installers, and Repairers
• Computer, Automated Teller, and Office Machine Repairers
• Electrical and Electronics Repairers, Commercial and Industrial Equipment
• Electrical and Electronics Repairers, Powerhouse, Substation, and Relay
• Avionics Technicians
• Electronic Equipment Installers and Repairers, Motor Vehicles
• Electrical and Electronics Installers and Repairers, Transportation Equipment

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.

Electronics Mechanics and Servicers sit at the 45th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Electronics Mechanics and Servicers rank in the 45th 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Keeping records of maintenance and repair work.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Electronics Mechanics and Servicers". https://singulariki.com/gradient/7421-electronics-mechanics-and-servicers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.