Electrical and Electronic Equipment Assemblers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Electrical and Electronic Equipment Assemblers (ISCO-08 8212) score an average of 0.28 on a 0–1 exposure scale — more exposed than about 52% 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 Minimal 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Recording production and operational data on specified forms;”

Scores 0.60 on the 2025 scale. The task of "Recording production and operational data on specified forms" involves structured data entry and documentation, making it partially automatable using Generative AI. Semantically similar tasks, such as "Maintaining required records of process progress" with an adjusted score of 0.45 and "Documenting completed drilling works" with a score of 0.495, show moderate potential for automation due to their reliance on routine documentation and data management. Generative AI can assist by automating data input and organizing documentation through templates. However, human oversight is essential for ensuring accuracy, context understanding, and handling exceptions. Given these task characteristics and the high technological infrastructure in Poland, the adjusted score of 0.45 reflects the balance between the potential for AI assistance in structured processes and the necessity for human verification and contextual expertise in production settings.

Moving fastest, 2023 → 2025

“Inspecting and testing completed components and assemblies, wiring installations and circuits and rejecting faulty assembly components;”

Model capability on this task changed by +0.16 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 8212, 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.

• Coil Winders, Tapers, and Finishers
• Etchers and Engravers
• Electrical and Electronic Equipment Assemblers
• Electromechanical Equipment Assemblers

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Electrical and Electronic Equipment Assemblers sit at the 52nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Electrical and Electronic Equipment Assemblers rank in the 52nd 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 0.08 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Recording production and operational data on specified forms;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Electrical and Electronic Equipment Assemblers". https://singulariki.com/gradient/8212-electrical-and-electronic-equipment-assemblers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.