Telecommunications Engineers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Telecommunications Engineers (ISCO-08 2153) score an average of 0.48 on a 0–1 exposure scale — more exposed than about 86% 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 7 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 0.61 on the 2025 scale. Designing and developing signal processing algorithms requires significant expertise in both mathematics and domain-specific knowledge, akin to tasks like designing new speech recognition algorithms, which had a moderate to high automation score of 0.7125. Although AI can assist with some aspects, such as prototyping, suggesting algorithmic improvements, or processing data to identify patterns, the creative and innovative aspects of algorithm design are currently beyond full automation. This task also involves implementation through hardware and software selection, which requires significant human expertise similar to creating and managing content management systems with an automation score of 0.7. The task's complexity and the need for iterative development and testing processes indicate that, while AI can aid significantly, especially in structured and repetitive components, human oversight remains critical, notably in a specialized field like signal processing. Given AI's advanced capabilities and the context of a high-income country with prevalent technology access, an adjusted score of 0.58 balances generative AI's potential contributions with the indispensable role of human creativity and oversight.

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 +0.32 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 2153, 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

In context

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

Telecommunications Engineers sit at the 86th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Telecommunications Engineers rank in the 86th 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 0.15 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 — "Telecommunications Engineers". https://singulariki.com/gradient/2153-telecommunications-engineers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.