How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is less exposed to automation, and meaningful change looks likely over the next 5 to 10 years.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 0.0 to 12.0
Physical / roboticsrange 10.0 to 34.0
When the change is likely
Partial, 5 to 10 yearslocal demand declining
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Use your 5 to 10 year runway
Change here is gradual. Build resilient skills steadily and watch the signals rather than waiting for a shock.
2
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
3
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
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Which of my tasks are exposed
Assemble and install components of timepieces to complete mechanisms, using watchmakers' tools and loupes.
22.0
Observe operation of timepiece parts and subassemblies to determine accuracy of movement, and to diagnose causes of defects.
100.0
Test operation and fit of timepiece parts and subassemblies, using electronic testing equipment, tweezers, watchmakers' tools, and loupes.
22.0
Replace specified parts to repair malfunctioning timepieces, using watchmakers' tools, loupes, and holding fixtures.
22.0
Disassemble timepieces such as watches, clocks, and chronometers so that repairs can be made.
22.0
Clean and lubricate timepiece parts and assemblies, using solvents, buff sticks, and oil.
22.0
Examine components of timepieces such as watches, clocks, or chronometers for defects, using loupes or microscopes.
100.0
Bend parts, such as hairsprings, pallets, barrel covers, and bridges, to correct deficiencies in truing or endshake, using tweezers.
22.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Observe operation of timepiece parts and subassemblies to determine accuracy of movement, and to diagnose causes of defects.
Task: Examine components of timepieces such as watches, clocks, or chronometers for defects, using loupes or microscopes.
Local demand is declining
How this score is calculated
Map Timing Device Assemblers and Adjusters to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 0.0, robotics 22.0.
The timeline comes from the higher axis (22.0), shifted for local demand (declining), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.