Headline risk
38%
High RiskAnimal control workers
United States AI Work Index tracks this occupation on the shared structural baseline and then layers on local demand resilience, wages, and confidence.
Why This Score
Share of job tasks that overlap with current AI capabilities
Median annual wage
Projected employment change over 10 years
Typical preparation needed for this occupation
Occupation profile
Handle animals for the purpose of investigations of mistreatment, or control of abandoned, dangerous, or unattended animals.
Task evidence
100% weighted task match · 0% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 45,830
Employment 2024
12.2K
Projected Change (2024–34)
3.9%
Openings (2024–34)
1.3K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Write reports of activities, and maintain files of impoundments and dispositions of animals. AI use: 0%
- 2. Investigate reports of animal attacks or animal cruelty, interviewing witnesses, collecting evidence, and writing reports. AI use: 0%
- 3. Educate the public about animal welfare, and animal control laws and regulations. AI use: 0%
- 4. Answer inquiries from the public concerning animal control operations. AI use: 0%
- 5. Examine animals for injuries or malnutrition, and arrange for any necessary medical treatment. AI use: 0%
- 6. Prepare for prosecutions related to animal treatment, and give evidence in court. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 39.4 · 93K employed
Under 25: 9% · 25–54: 74% · 55+: 17%
Related
No direct US role match is available yet for this occupation.
Source coverage
11/11 source families · O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Mapping quality
major_group_fallback · employment series present
Narrative & sources
Published limitations
This page shows the local country layer, not realised individual job outcomes. The global structural baseline is shared across countries; only the local demand and wage layer changes here.
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.