Aircraft Engine Mechanics and Repairers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Aircraft Engine Mechanics and Repairers (ISCO-08 7232) score an average of 0.19 on a 0–1 exposure scale — more exposed than about 31% 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Maintaining repair logs, documenting all preventive and corrective aircraft maintenance;”

Scores 0.39 on the 2025 scale. The task of maintaining repair logs and documenting preventive and corrective aircraft maintenance involves structured documentation processes that Generative AI can assist in significantly. Similar tasks in the context, such as maintaining technological process documentation and updating logbooks, have varying automation scores, with technical documentation tasks showing moderate potential for AI assistance. The task demands thoroughness and accuracy, which AI can support by automating routine entries and ensuring consistency, though human oversight is crucial for understanding context-specific narratives and making judgment calls. Given the task's structured nature and potential for automation, but balanced by the critical aspect of human expertise for safety and regulatory compliance, an adjusted score of 0.4 is appropriate. This considers the support role of AI and the necessity of human intervention, aligning closely with tasks in the same semantic cluster and context.

Moving fastest, 2023 → 2025

“Fitting, examining, testing and servicing aircraft engines;”

Model capability on this task changed by +0.12 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 7232, 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
• Aircraft Mechanics and Service Technicians

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.

Aircraft Engine Mechanics and Repairers sit at the 31st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Aircraft Engine Mechanics and Repairers rank in the 31st 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Maintaining repair logs, documenting all preventive and corrective aircraft maintenance;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Aircraft Engine Mechanics and Repairers". https://singulariki.com/gradient/7232-aircraft-engine-mechanics-and-repairers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.