How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Design customer interface of online storefront, using web programming or e-commerce software.76
- Measure and analyze Web site usage data to maximize search engine returns or refine customer interfaces.76
- Deliver e-mail confirmation of completed transactions and shipment.75
- Purchase new or used items from online or physical sources for resale via retail or auction Web site.75
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Deliver e-mail confirmation of completed transactions and shipment.01
- Create, manage, or automate orders or invoices, using order management or invoicing software.02
- Purchase new or used items from online or physical sources for resale via retail or auction Web site.03
- Compose descriptions of merchandise for posting to online storefront, auction sites, or other shopping Web sites.04
- Upload digital media, such as photos, video, or scanned images to online storefront, auction sites, or other shopping Web sites.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
