Packaging and Filling Machine Operators and Tenders
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is highly exposed to automation, and the bigger shift looks near-term.
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 19.5
Physical / roboticsrange 58.3 to 82.3
When the change is likely
Near-term, highlocal demand stable
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Act within the next 1 to 2 years
Displacement for this role is near-term. Start a concrete reskilling plan now, while you still have income and leverage.
2
Move up from the task to the system
Robotics targets the physical work. Shift toward supervising, maintaining, or programming the automation instead of competing with it.
3
Pivot toward Packers and Packagers, Hand
It is skill-adjacent and less exposed (58.3 vs 70). A lateral move now beats a forced one later.
4
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.
5
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
Keep the monitor on
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Which of my tasks are exposed
Attach identification labels to finished packaged items, or cut stencils and stencil information on containers, such as lot numbers or shipping destinations.
70.3
Sort, grade, weigh, and inspect products, verifying and adjusting product weight or measurement to meet specifications.
70.3
Stop or reset machines when malfunctions occur, clear machine jams, and report malfunctions to a supervisor.
70.3
Observe machine operations to ensure quality and conformity of filled or packaged products to standards.
70.3
Remove finished packaged items from machine and separate rejected items.
70.3
Monitor the production line, watching for problems such as pile-ups, jams, or glue that isn't sticking properly.
70.3
Inspect and remove defective products and packaging material.
70.3
Start machine by engaging controls.
70.3
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Attach identification labels to finished packaged items, or cut stencils and stencil information on containers, such as lot numbers or shipping destinations.
Task: Sort, grade, weigh, and inspect products, verifying and adjusting product weight or measurement to meet specifications.
Lower-risk adjacent roles
Packers and Packagers, Hand
58.3
Machine Feeders and Offbearers
60.0
Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders
62.3
Paper Goods Machine Setters, Operators, and Tenders
66.0
Adhesive Bonding Machine Operators and Tenders
67.1
Cutting and Slicing Machine Setters, Operators, and Tenders
68.2
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Packaging and Filling Machine Operators and Tenders 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 7.5, robotics 70.3.
The timeline comes from the higher axis (70.3), 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.