Headline risk
12%
Low RiskPharmacy aides
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
Record drugs delivered to the pharmacy, store incoming merchandise, and inform the supervisor of stock needs. May operate cash register and accept prescriptions for filling.
Task evidence
100% weighted task match · 4% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 37,000
Employment 2024
41.1K
Projected Change (2024–34)
-0.1%
Openings (2024–34)
6.1K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Greet customers and help them locate merchandise. AI use: 0%
- 2. Answer telephone inquiries, referring callers to pharmacist when necessary. AI use: 0%
- 3. Operate cash register to process cash or credit sales. AI use: 0%
- 4. Accept prescriptions for filling, gathering and processing necessary information. AI use: 0%
- 5. Restock storage areas, replenishing items on shelves. AI use: 0%
- 6. Unpack, sort, count, and label incoming merchandise, including items requiring special handling or refrigeration. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 33.9 · 373K employed
Under 25: 25% · 25–54: 64% · 55+: 11%
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.