Electrical Line Installers and Repairers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Electrical Line Installers and Repairers (ISCO-08 7413) score an average of 0.15 on a 0–1 exposure scale — more exposed than about 18% 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

“Identifying defective sectionalizing devices, circuit breakers, fuses, voltage regulators, transformers, switches, relays or wiring, using wiring diagrams and electrical-testing instruments.”

Scores 0.27 on the 2025 scale. The task of identifying defective sectionalizing devices, circuit breakers, and other electrical components involves hands-on diagnostic work, direct physical interaction, and nuanced problem-solving that current Generative AI cannot fully automate. The automation score for semantically similar tasks, such as testing and diagnosing electrical circuits, ranges from 0.26 to 0.35, reflecting their manual and interpretative nature. Although Generative AI can assist by interpreting diagnostic data and offering potential solutions, the actual identification and handling of defects, especially in complex electrical systems, require significant human expertise. Given the capability of AI to augment but not replace the human element in this domain and acknowledging the access to AI tools in a high-income country like Poland, the adjusted score of 0.29 considers both the potential assistance AI can provide and the irreplaceable role of human skills in this task.

Moving fastest, 2023 → 2025

“Adhering to safety practices and procedures, such as checking equipment regularly and erecting barriers around work areas;”

Model capability on this task changed by +0.13 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 7413, 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
• Electrical Power-Line Installers and Repairers

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.

Electrical Line Installers and Repairers sit at the 18th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Electrical Line Installers and Repairers rank in the 18th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Identifying defective sectionalizing devices, circuit breakers, fuses, voltage regulators, transformers, switches, relays or wiring, using wiring diagrams and electrical-testing instruments.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Electrical Line Installers and Repairers". https://singulariki.com/gradient/7413-electrical-line-installers-and-repairers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.