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 14.9
Physical / roboticsrange 27.0 to 51.0
When the change is likely
Partial, 5 to 10 yearslocal demand stable
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
Apply coatings, such as paint, ink, or lacquer, to protect or decorate workpiece surfaces, using spray guns, pens, or brushes.
39.0
Examine finished surfaces of workpieces to verify conformance to specifications and retouch any defective areas.
39.0
Clean and maintain tools and equipment, using solvents, brushes, and rags.
39.0
Read job orders and inspect workpieces to determine work procedures and materials required.
39.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Apply coatings, such as paint, ink, or lacquer, to protect or decorate workpiece surfaces, using spray guns, pens, or brushes.
Task: Examine finished surfaces of workpieces to verify conformance to specifications and retouch any defective areas.
How this score is calculated
Map Painting, Coating, and Decorating Workers to its standard O*NET task list (4 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 2.9, robotics 39.0.
The timeline comes from the higher axis (39.0), shifted for local demand (stable), 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.