JobBreak

Community and Social Service · United States (national)

Religious Workers, All Other

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 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
36.2 of 100
AI / softwarerange 24.2 to 48.2
0.0 of 100
Physical / roboticsrange 0.0 to 12.0

When the change is likely

change likely 0y 5y 10y 15y 20+y

Near-term, high local demand declining

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
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.

Which of my tasks are exposed

Longer, orange bars are more automatable; short green bars are resistant.

What is driving my result

lowers risk
raises risk
Local demand is declining

How this score is calculated

  1. Map Religious Workers, All Other to its standard O*NET task list (0 core tasks).
  2. 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.
  3. The two axis scores are the task averages: AI 36.2, robotics 0.0.
  4. The timeline comes from the higher axis (36.2), shifted for local demand (declining), and seniority. It is read against your remaining working years.
  5. 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.

Check another job

Stay ahead of the curve. Get notified when the automation outlook for your job shifts.