Riggers and Cable Splicers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Riggers and Cable Splicers (ISCO-08 7215) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 9% 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

“Estimating the size, shape and weight of objects to be moved and deciding on the type of equipment to move them;”

Scores 0.16 on the 2025 scale. The task of estimating the size, shape, and weight of objects to be moved and deciding on the type of equipment to move them involves significant manual judgement, sensory perception, and physical interaction with objects, which Generative AI cannot automate. It closely resembles tasks such as "Arranging small items according to instructions..." (adjusted score: 0.165) and "Sorting loads before loading..." (adjusted score: 0.1625) that require human physical dexterity and decision-making, which AI can only partially support, typically in logistic planning or data analysis roles. Generative AI can assist in suggesting optimal equipment or routes, yet the core evaluation and decision aspects heavily rely on human expertise. In a high-income country like Poland, the AI can offer substantial logistical support, but the physical evaluation remains beyond its capabilities, justifying a slightly lower score within the range observed from similar tasks. Hence, while AI can augment planning, the core requirement of this task still limits its automation potential.

Moving fastest, 2023 → 2025

“Joining, repairing and fitting attachments to wires, ropes and cables;”

Model capability on this task changed by +0.02 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 7215, 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.

• Riggers

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.

Riggers and Cable Splicers sit at the 9th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Riggers and Cable Splicers rank in the 9th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Estimating the size, shape and weight of objects to be moved and deciding on the type of equipment to move them;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Riggers and Cable Splicers". https://singulariki.com/gradient/7215-riggers-and-cable-splicers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.