Motor Vehicle Mechanics and Repairers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Motor Vehicle Mechanics and Repairers (ISCO-08 7231) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 26% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Detecting and diagnosing faults in engines and parts;”

Scores 0.25 on the 2025 scale. The task of detecting and diagnosing faults in engines and parts requires a high degree of hands-on technical skill, practical experience, and sensory feedback—all areas where current Generative AI technologies are limited. The analysis of semantically similar tasks, such as "Diagnosing malfunctions and damages of machines and devices" (adjusted score: 0.25) and "Detecting irregularities in the operation of machines and devices" (adjusted score: 0.2875), reflects this inherent challenge in fully automating such tasks. While AI can provide diagnostic support and guide technicians through troubleshooting via data analysis and predictive maintenance protocols, it cannot fully substitute the nuanced physical interaction and judgment skills of a human expert. The manual inspection and adjustment aspects are crucial components of this job, with AI serving a complementary role. This score reflects the limited automation potential, acknowledging that AI may assist but cannot replace the human workforce in executing this complex, tactile task. Additionally, the context of the task being performed in a high-income country like Poland, with good access to technology and digital infrastructure, supports the AI's ancillary role but not full automation.

Moving fastest, 2023 → 2025

“Reassembling engines and parts after being repaired.”

Model capability on this task changed by +0.08 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 7231, 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.

• Automotive Service Technicians and Mechanics
• First-Line Supervisors of Mechanics, Installers, and Repairers
• Bus and Truck Mechanics and Diesel Engine Specialists
• Automotive Body and Related Repairers
• Tire Repairers and Changers
• Outdoor Power Equipment and Other Small Engine Mechanics
• Motorboat Mechanics and Service Technicians
• Automotive Glass Installers and Repairers

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.

Motor Vehicle Mechanics and Repairers sit at the 26th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Motor Vehicle Mechanics and Repairers rank in the 26th 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: "Detecting and diagnosing faults in engines and parts;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Motor Vehicle Mechanics and Repairers". https://singulariki.com/gradient/7231-motor-vehicle-mechanics-and-repairers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.