Electronics Engineers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Electronics Engineers (ISCO-08 2152) score an average of 0.42 on a 0–1 exposure scale — more exposed than about 79% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 2 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Designing and developing signal processing algorithms and implementing these through appropriate choice of hardware and software;”

Scores 0.60 on the 2025 scale. The task of designing and developing signal processing algorithms and implementing these through appropriate hardware and software requires a blend of both technical expertise and creative problem-solving, similar to designing new technologies and developing algorithms, tasks that received scores ranging from around 0.55 to 0.75. Generative AI can assist significantly in the development and simulation phase by providing design suggestions, optimizing algorithms, and processing large datasets efficiently. However, implementing these algorithms through hardware and software demands human oversight for nuanced decision-making, understanding specific system constraints, and performing hands-on adjustments, akin to tasks like developing microprocessor systems and speech recognition algorithms. This combination of AI assistance in data processing and human necessity for creative and technical interventions suggests a moderate level of automation potential for this task. Given the technological context of Poland, where infrastructure supports AI-assisted development but necessitates human intervention for specialized tasks, an adjusted score of 0.56 reflects the significant assistance AI can provide while acknowledging the indispensable role of human expertise in this domain.

Moving fastest, 2023 → 2025

“Designing and developing signal processing algorithms and implementing these through appropriate choice of hardware and software;”

Model capability on this task changed by +0.30 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 2152, 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.

• Electronics Engineers, Except Computer
• Radio Frequency Identification Device Specialists
• Computer Hardware Engineers

In context

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

Electronics Engineers sit at the 79th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Electronics Engineers rank in the 79th 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 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.14 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Designing and developing signal processing algorithms and implementing these through appropriate choice of hardware and software;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Electronics Engineers". https://singulariki.com/gradient/2152-electronics-engineers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.