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AI Automation Index
Will AI Replace Door-to-Door Sales Workers, News and Street Vendors, and Related Workers?
Task-level automation exposure for Door-to-Door Sales Workers, News and Street Vendors, and Related Workers: 12 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
45%
weighted task index
12 of 12 tasks scored
Median Salary
$41,380 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
2,760 people hold this job — size, not growth
Projected 2025–35
-10.4%
employment change
4,800 openings a year · BLS line item
Pay by state
BLS OEWS publishes 14 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$32,320 – $46,380
Highest medians
Missouri $46,380
Texas $46,040
New Jersey $44,370
Indiana $39,870
California $39,520
Lowest medians
Virginia $32,320
Kansas $32,970
Maryland $34,290
Ohio $36,210
Michigan $36,700
State figures come from the 41-9091 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 45%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 11%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 26%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 18%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 0/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
12 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 35%
4 tasks
Medium exposure (≥ 35%) 36%
4 tasks
Low exposure (below 35%) 28%
4 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 08/2018 · Occupational Expert
Contact customers to ensure their satisfaction with products or services.
- New 08/2018 · Occupational Expert
Train new recruits or other employees.
Constituent Tasks
#13233 · Core · 38% automatable
Explain products or services and prices and demonstrate use of products.
interpersonal — Technical explanation to customers needs live interaction
Explain technical product or service information to customers. Demonstrate products to consumers.
#13238 · Core · 68% automatable
Develop prospect lists.
data analysis — AI analyzes market data to identify prospects
Identify potential customers.
#13231 · Core · 55% automatable
Deliver merchandise and collect payment.
decision support — AI can arrange deliveries; exceptions need human handling
Arrange delivery of goods or services. Process sales or other transactions.
#13232 · Core · 70% automatable
Write and record orders for merchandise or enter orders into computers.
interpersonal — Order capture automatable; customer interaction partly human
Take product orders from customers.
#13235 · Core · 48% automatable
Arrange buying parties and solicit sponsorship of such parties to sell merchandise.
decision support — Campaign coordination needs human judgment and relationships
Contact current or potential customers to promote products or services. Coordinate sales campaigns.
#13236 · Core · 68% automatable
Answer questions about product features and benefits.
information retrieval — Product questions answered from knowledge base
Answer customer questions about goods or services.
#13239 · Core · 15% automatable
Distribute product samples or literature that details products or services.
physical manipulation — Physically distributing samples and literature
Distribute promotional literature or samples to customers.
#13237 · Core · 44% automatable
Circulate among potential customers or travel by foot, truck, automobile, or bicycle to deliver or sell merchandise or services.
decision support — AI can arrange deliveries; exceptions need human handling
Sell products or services. Arrange delivery of goods or services.
#21075 · Core · 32% automatable
Persuade customers to purchase merchandise or services.
interpersonal — Closing sales requires persuasion and accountability
Sell products or services.
#13241 · Core · 6% automatable
Set up and display sample merchandise at parties or stands.
physical manipulation — Setting up displays requires hands-on placement
Set up merchandise displays.
#13240 · Core · 65% automatable
Order or purchase supplies.
decision support — Merchandise purchasing aided; human owns buying decisions
Purchase stocks of merchandise or supplies.
#13242 · Supplemental · 10% automatable
Stock carts or stands.
physical manipulation — Physically moving and shelving stock items
Stock products or parts.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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