Door-to-Door Sales Workers, News and Street Vendors, and Related Workers
41-9091.00Median wage $41,380/yr2,760 employed (US)Rank #305 of 923 scored · top 33% by substitution
Sell goods or services door-to-door or on the street.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution34
Exposure26
Augmentation42
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
12 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
17%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%28
panel mean rating 2.1/5 → substitution pressure 28/100
Technical feasibility todayw 20%22
panel mean rating 1.9/5 → substitution pressure 22/100
Cost vs. human wagew 15%30
panel mean rating 2.2/5 → substitution pressure 30/100
Adoption barriersw 20%inverted — strong barriers lower the score65
panel mean rating 2.4/5 (barrier strength) → substitution pressure 65/100
Sector adoption velocityw 10%16
panel mean rating 1.7/5 → substitution pressure 16/100
Task breakdown (12 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Develop prospect lists.
80CI 74–86 · exposure 75 · augmentation 75 · importance 4.4/5 · click for rater detail
Develop prospect lists.
80| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Sales teams across enterprise, mid-market, and SMB software are rapidly adopting AI-driven prospect list tools in production, with widespread industry uptake reflected in mainstream sales tech stacks. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Door-to-door and street vending are low-digitization, small-firm-dominated sectors where such tools are rarely adopted despite availability. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI prospect lists substantially augment a salesperson's productivity by surfacing and pre-qualifying leads, allowing the human to focus on prioritization and customized outreach rather than manual research. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where full automation isn't adopted, AI-based lead databases and filtering tools meaningfully speed up and improve prospect list creation for individual workers. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Prospect list development is heavily data-driven and can be largely automated using current AI systems that aggregate public records, company information, social media, and behavioral signals; however, qualification and context-specific filtering typically still benefit from human judgment, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 4/5 | AI tools can scrape public data, aggregate demographic/business databases, and generate targeted prospect lists with minimal human input, meeting or exceeding the time-saving threshold for this specific subtask.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Data privacy regulations (GDPR, CCPA) and terms of service on scraping create some friction, but no legal requirement for human certification exists, and many businesses routinely automate this task today with minimal friction. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory requirement mandates human involvement in compiling prospect lists. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated prospect list generation via SaaS tools costs pennies per prospect versus hours of manual research by a salesperson at loaded wages, achieving an order-of-magnitude cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated list generation via SaaS tools costs a fraction of a cent per lead compared to hours of manual research by a human canvasser. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (LinkedIn Sales Navigator, Apollo.io, Clearbit, Hunter.io) reliably scrape and compile prospect lists at scale in production; they effectively automate list generation with acceptable error rates for commercial use. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Commercial lead-generation and CRM enrichment products (e.g., ZoomInfo, Apollo, Clearbit) reliably generate prospect lists at scale in production today. |
Write and record orders for merchandise or enter orders into computers.
77CI 70–84 · exposure 75 · augmentation 88 · importance 4.3/5 · click for rater detail
Write and record orders for merchandise or enter orders into computers.
77| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Fast adoption is evident in retail, e-commerce, food delivery, and logistics (voice-based ordering, mobile apps, POS integration). Street and door-to-door vendors lag slightly due to informal sector presence, but even there digital ordering is expanding rapidly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Door-to-door sales and street vending are low-digitization, small-scale sectors where adoption of automated order systems lags far behind office-based retail or e-commerce. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI voice-to-text and intelligent form-filling assistants meaningfully augment vendors by reducing manual writing time and error, freeing them to focus on customer interaction and upselling while the system handles accurate capture and entry. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Mobile apps and voice-to-text tools already let vendors quickly log orders, reducing errors and administrative burden while the salesperson remains the primary customer interface. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can reliably capture order details from text or voice input, validate SKUs/inventory, and enter structured data into systems with minimal human intervention. The task requires no creative judgment or complex negotiation—purely data capture and entry, which achieves well over 50% time savings with existing tools like voice-to-order and OCR systems. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording and entering orders into a computer system is a structured data-entry task that AI/automation (voice-to-text, OCR, order-entry bots) can perform with substantial time savings, though verifying accuracy with customers may need a human check. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist to automating order entry itself; most friction is customer preference for human interaction during the sale and organizational inertia in street vending environments. No licensing requirement applies to the order-writing task itself. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory barriers prevent automating simple order recording and data entry. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI systems for order entry cost pennies per transaction after initial setup, while a human door-to-door or street vendor worker costs $15–25+ per hour. Even with oversight, the cost-per-order ratio is at least 10–20× cheaper for AI. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated order entry via mobile apps or voice assistants costs a fraction of a cent per transaction compared to a human's time to write and log an order manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (e-commerce platforms, voice assistants, order management systems) routinely automate order capture at scale in retail and food service sectors. Performance is reliable in structured contexts, though edge cases (ambiguous product names, handwriting) still require human review at modest rates. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Order-entry and voice-transcription-to-database systems are mature and widely deployed in retail and sales contexts, though door-to-door contexts may still rely on manual paper/mobile entry lacking full integration. |
Answer questions about product features and benefits.
52CI 41–64 · exposure 50 · augmentation 63 · importance 4.2/5 · click for rater detail
Answer questions about product features and benefits.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | E-commerce and call-center automation of product Q&A is moderately mature, but door-to-door sales is a labor-intensive, geographically dispersed sector with low digitization. Pilots exist but large-scale replacement via autonomous agents remains uncommon in this specific channel. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door and street vending are low-digitization, small-scale, physically embedded occupations with minimal AI adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist sales workers by providing real-time product feature/benefit prompts, comparison data, and talking points during conversations, thereby boosting their productivity and accuracy without removing them from the customer interaction loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Sales workers can use AI-generated scripts, FAQs, or mobile assistants to prep answers and improve consistency, offering moderate productivity gains. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Current AI systems (chatbots, LLMs) can reliably answer factual questions about product features and benefits using knowledge bases or product documentation. However, significant personalization, objection handling, and context-aware selling still require human judgment, so only partial task automation meets the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI chatbots can answer straightforward product questions well, but in-person door-to-door or street vending contexts require real-time physical presence, tone-reading, and improvisation that current systems can't fully replicate. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers apply to automated product information answering. However, door-to-door sales involves direct human contact and relationship-building, and consumer trust/preference for talking to a real person provides some organizational friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer preference for human interaction and the physical/mobile nature of the job creates moderate friction against pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | LLM-based systems and deployed chatbots cost pennies per query after infrastructure amortization, while a door-to-door sales worker's fully loaded wage is typically $40–60/hour. AI is substantially cheaper once deployed, though integration and training overhead must be accounted for. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI text/voice answering is cheap, replicating the physical presence and improvisational rapport of door-to-door selling requires human labor or robotics that aren't cost-competitive yet. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed conversational AI and FAQ systems demonstrably handle product feature/benefit questions in production (e-commerce sites, customer support chatbots). Performance is generally reliable for factual content, though some complex or novel questions still trigger escalation to humans. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed chatbots handle product Q&A in retail/e-commerce, but no mature product performs this within the physical, face-to-face selling context described here. |
Order or purchase supplies.
48CI 30–66 · exposure 45 · augmentation 50 · importance 3.7/5 · click for rater detail
Order or purchase supplies.
48| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Door-to-door sales and street vendors are typically small-scale, low-digitization, informal operations with limited enterprise infrastructure. Adoption of AI-driven procurement is lagging in these sectors compared to retail and distribution. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This occupation is low-digitization and small-scale, so despite the task's technical automatability, actual adoption among street/door-to-door vendors is minimal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by suggesting reorder quantities based on sales patterns, flagging low inventory, or recommending vendors—genuinely useful augmentation that maintains human control over purchasing decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help track inventory needs and suggest reorder timing/quantities, moderately aiding vendors who choose to use such tools, though most in this occupation likely don't. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Ordering supplies requires decision-making about what to order, quantity, and vendor selection based on sales context and inventory—tasks with significant judgment elements. AI could automate routine reorder workflows for predictable consumables, but the variability in sales worker contexts and supply needs prevents end-to-end automation at the 50% threshold. |
| Task automatability | claude-sonnet-5 | 4/5 | Ordering/purchasing supplies is a structured, repetitive transaction (checking stock levels, placing orders via vendor portals or emails) that AI systems and procurement agents can largely automate given catalog and inventory data access. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Most supply ordering is already digitized in larger organizations (medium friction), but many door-to-door vendors operate independently with simpler, more manual processes. Organizational systems and vendor relationships create modest friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory barrier prevents automating supply ordering; it's a routine administrative task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of building and maintaining an AI ordering system, plus human oversight for exceptions and judgment calls, likely exceeds the wage cost of a sales worker spending 10–20 minutes ordering supplies, especially for small vendors and independent contractors. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | For small independent vendors, setting up an AI ordering system may cost more than simply calling a supplier themselves, though at larger scale automated reordering is much cheaper than manual purchasing labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While e-procurement and inventory-management systems exist, they typically require human initiation and judgment. No deployed product reliably handles the full decision loop of what a door-to-door sales worker needs to order without substantial manual oversight and contextual input. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Procurement automation tools and AI-driven inventory reordering systems exist and are used in retail/logistics, but for small-scale door-to-door or street vendors, such integrated systems are rarely deployed in practice. |
Explain products or services and prices and demonstrate use of products.
23CI 14–33 · exposure 20 · augmentation 50 · importance 4.7/5 · click for rater detail
Explain products or services and prices and demonstrate use of products.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Door-to-door and street vending remain low-digitization, physical-presence sectors with predominantly small operators and limited automation investment. Digital channels (e-commerce, online demos) have displaced some traditional sales, but field sales workers themselves show laggard AI adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door and street vending is a low-digitization, small-scale, physically embedded sector with minimal AI agent deployment in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating real-time product information, pricing comparisons, and talking points that a human sales worker accesses via mobile device, or by drafting follow-up messaging; however, the face-to-face persuasion and physical demonstration remain human-driven, offering moderate rather than transformative productivity gain. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help vendors with talking points, pricing info, and practice scripts via mobile apps, offering moderate but not transformative productivity gains. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate product explanations and pricing information at scale, the core task requires real-time interpersonal persuasion, live product demonstration, handling objections, and building rapport with diverse customers face-to-face—capabilities current AI lacks. Even with embodied robots, deployment for field sales remains infeasible today. |
| Task automatability | claude-sonnet-5 | 2/5 | Explaining products/prices can be scripted or handled via chatbots, but the physical door-to-door demonstration and in-person persuasion component resists full automation today.5-x |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: customers in many markets expect human interaction and trust, liability for product claims and demonstrations falls on the vendor, many jurisdictions regulate door-to-door sales, and the physical, uncontrolled nature of street vending requires presence and judgment that AI cannot provide without costly robotics and legal uncertainty. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer preference for face-to-face interaction and the physical nature of door-to-door demos create moderate friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI-assisted tools (chatbots, content generation) cost pennies per interaction, but delivering the full task—physical presence, live demonstration, handling rejection, and closing sales—requires either expensive robotic hardware or falls back to human sales workers, making end-to-end automation more expensive than hiring. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI phone/chat systems are cheap for explaining products remotely, but replicating in-person demos requires robotics or human labor, keeping overall cost comparable to a human vendor for this specific task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs door-to-door sales with live product demonstration and customer negotiation in the field. Chatbots can answer questions online, but uncontrolled outdoor environments, physical product handling, and the need for human judgment make this research-stage only. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Voice assistants and chatbots can explain products in retail/telesales contexts, but no deployed product performs in-person physical demonstrations to walk-up customers reliably. |
Persuade customers to purchase merchandise or services.
23CI 18–28 · exposure 16 · augmentation 38 · importance 3.8/5 · click for rater detail
Persuade customers to purchase merchandise or services.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Door-to-door and street vending remain low-digitization, small-firm sectors with minimal AI adoption; most transaction displacement has occurred via e-commerce rather than automation of the sales persuasion function itself. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door and street vending sectors are low-digitization, small-scale, and physically embodied, showing minimal AI agent adoption in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating personalized talking points, suggesting objection responses, or analyzing customer data to target high-probability prospects, but the human salesperson remains essential for real-time delivery and relationship closure. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with lead lists, scripts, or product information lookup beforehand, but offers little real-time support during the actual in-person persuasion encounter. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate persuasive text and personalized pitches, the task fundamentally requires real-time interpersonal negotiation, reading subtle social cues, and adaptive rapport-building in face-to-face contexts that current AI cannot reliably replicate end-to-end. Automating even 50% of door-to-door sales effectiveness would require autonomous physical presence and genuine social intelligence beyond deployed systems. |
| Task automatability | claude-sonnet-5 | 2/5 | Persuasion here relies on physical presence, spontaneous rapport-building, and in-person trust-building that current AI cannot replicate end-to-end; chatbots can support some remote sales scripting but not the core door-to-door interaction.','ok' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: consumer expectations and trust strongly favor human contact, psychological safety in door-to-door contexts requires human judgment, and many jurisdictions impose licensing or bonding requirements on sales representatives that legally bind responsibility to a human agent. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but strong practical/organizational barriers exist since the task depends on physical presence, human trust signals, and on-the-spot social judgment that AI cannot supply. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of deploying physical sales agents (robotics or remote operation) plus fallback human oversight vastly exceeds the loaded wage of a human door-to-door worker, and AI cannot yet replicate conversion rates. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Physical robotic presence or autonomous agents to replace an in-person vendor would be far costlier than a low-wage human seller in this role today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs door-to-door or street persuasion in production at scale. Chatbots and email automation exist for indirect sales, but the in-person, unscripted, objection-handling nature of this task remains outside the scope of production AI systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs autonomous face-to-face door-to-door or street-vending persuasion; this remains firmly outside current AI product capability. |
Circulate among potential customers or travel by foot, truck, automobile, or bicycle to deliver or sell merchandise or services.
18CI 15–21 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Circulate among potential customers or travel by foot, truck, automobile, or bicycle to deliver or sell merchandise or services.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of autonomous delivery is currently limited to controlled pilots in select urban areas and e-commerce warehouses; most door-to-door and street vending remains human-performed across the broader economy with slow transition to automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door sales and street vending are low-digitization, physically embodied occupations with minimal AI adoption; this sector shows negligible movement toward AI-driven automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance to door-to-door sales workers in the core task of circulating and delivering merchandise, as it cannot augment the physical mobility, interpersonal negotiation, or in-person customer engagement that define the job. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route planning, customer targeting, or lead generation, but it does not meaningfully augment the core physical act of traveling and delivering/selling in person. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires physical presence at customer locations to deliver merchandise or services. Current AI systems cannot operate autonomous mobile hardware at the scale and reliability needed to replace foot traffic, truck routes, or bicycle delivery in the real world without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, mobility, and in-person interaction to travel and deliver/sell goods, which current AI cannot perform as it has no physical embodiment for general-purpose door-to-door mobility and delivery.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical delivery to homes and businesses typically does not require explicit licensing, but liability concerns, customer preference for human interaction in sales contexts, neighborhood safety regulations, and organizational risk aversion create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically restricts this task, but physical, safety, and liability concerns around autonomous door-to-door delivery/sales create moderate practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous delivery systems (robots, drones, vehicles) require substantial upfront capital and ongoing maintenance that currently exceeds the loaded wage of human delivery workers in most markets, though costs are declining. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any hypothetical automation (robotics, autonomous vehicles) would be far costlier than a human vendor at present. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While autonomous delivery robots and drones exist in limited pilot form, no deployed product reliably performs end-to-end door-to-door sales circulation and merchandise delivery at production scale across diverse neighborhoods and customer interactions today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product travels door-to-door on foot or by vehicle to sell or deliver merchandise autonomously; delivery robots and drones exist only in narrow pilot contexts, not for general street vending or door-to-door sales. |
Arrange buying parties and solicit sponsorship of such parties to sell merchandise.
15CI 5–25 · exposure 8 · augmentation 38 · importance 4.2/5 · click for rater detail
Arrange buying parties and solicit sponsorship of such parties to sell merchandise.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Door-to-door and direct sales sectors are slow to digitize and adopt agent-based AI. Most activity remains human-driven and localized, with limited evidence of production AI agents replacing this work in real deployments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door and direct sales is a low-digitization, small-scale, physically-dependent sector with minimal AI agent adoption in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can meaningfully assist by identifying target sponsors, drafting invitation copy, managing prospect databases, and tracking sponsorship commitments. These functions raise human sales productivity, though the persuasion and negotiation remain human-led. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, invitation drafting, or customer list management, but offers little assistance for the core in-person solicitation and persuasion work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with identifying prospects and drafting outreach, the core task—arranging and soliciting sponsorships from individuals—requires relationship-building, negotiation, and personal persuasion that current systems cannot perform end-to-end. Even with tool use, human presence and judgment remain essential. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, in-person relationship building, and persuasive live negotiation with hosts and attendees that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Sponsorship and party arrangement rely heavily on human trust, credibility, and relationship-building; customers strongly prefer direct human contact and negotiation. Social and organizational friction against full automation is substantial, and liability for failed commitments typically falls on the human. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but the task fundamentally depends on personal relationships, trust, and physical presence, creating strong organizational and social friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI assistance (prospect identification, message generation) reduces some planning overhead, but the human-led negotiation and relationship work still dominates task cost. Integration and oversight costs are modest compared to human labor, but savings remain partial. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this task, so no meaningful cost comparison exists; a human must be paid regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous party arrangement and sponsorship solicitation. AI can support these activities (CRM tools, email drafting) but does not yet execute the full task in production without human agency. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product organizes or hosts sales parties or solicits real-world sponsorships; this remains entirely a human, in-person activity. |
Set up and display sample merchandise at parties or stands.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Set up and display sample merchandise at parties or stands.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Door-to-door and street vending remain low-digitization, small-firm, physical-labor sectors with minimal automation infrastructure; current adoption of AI or robotics in these contexts is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door and street vending is a low-digitization, small-scale, physical retail sector with minimal AI/robotics adoption for physical merchandising tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI provides no meaningful assistance in the physical act of setting up or arranging merchandise displays at parties or stands; the task is almost entirely sensorimotor and spatial, not amenable to digital augmentation tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help plan optimal display layouts or generate marketing visuals beforehand, but offers little direct assistance during the physical act of setting up and arranging sample merchandise. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of merchandise in varied spatial contexts (parties, stands), dynamic interaction with human attendees, and aesthetic judgment about display arrangement—capabilities far beyond current AI systems' physical embodiment and real-world reasoning. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring transporting, arranging, and displaying tangible merchandise at a physical location, which current AI systems cannot perform.the underlying action is manual labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task occurs in customer-facing settings where human presence and judgment are preferred, and liability for damage or poor display falls on the vendor; minor friction exists but few hard legal barriers to automation attempt. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but the physical nature of the task (moving and arranging objects, aesthetic judgment) creates practical friction against non-human execution, though robotics could theoretically handle simple versions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any current solution (mobile robot, robotic arm, remote operation) would cost orders of magnitude more than paying a human sales worker to set up and display merchandise, especially for ad-hoc or small-scale events. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical setup task, so any AI cost comparison is moot; a human must physically perform it, making AI more expensive by default (infinite ratio since no AI option exists). |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic system or autonomous agent reliably sets up and displays merchandise in unstructured, person-filled environments like parties or market stands; this remains research-stage or requires heavy human supervision. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically sets up or displays merchandise; this remains entirely a human physical activity with no robotic or software product performing this in production. |
Stock carts or stands.
15CI 15–15 · exposure 0 · augmentation 0 · importance 2.7/5 · click for rater detail
Stock carts or stands.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Street vendors and door-to-door sales workers operate in informal, small-scale, low-digitization sectors with minimal capital investment. Adoption of physical automation in this space is negligible and unlikely to accelerate. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Street vending and informal retail are low-digitization, low-capital sectors with virtually no AI or robotics adoption for physical stocking tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful assistance with the physical act of stocking merchandise on carts or stands in real-world vendor environments. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for the physical act of loading and arranging goods on a cart or stand. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Stocking carts or stands requires physical manipulation of merchandise in varied outdoor/street environments with unpredictable layouts and merchandise types. Current AI systems cannot perform physical manipulation tasks end-to-end in real-world, unstructured settings. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically stocking a cart or stand with merchandise requires manual handling, transport, and arrangement of physical goods, which current AI systems cannot perform end-to-end without robotic embodiment far beyond off-the-shelf capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict legal barriers to automation, the task occurs in highly variable street/outdoor environments where small vendor operations predominate, creating natural organizational and practical friction against robotic deployment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human stock the cart, but practical barriers like need for physical dexterity, mobility, and lack of automation infrastructure make substitution unlikely soon. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Physical robotics capable of stocking tasks remain expensive to acquire and maintain. The cost of deploying such systems far exceeds the hourly wage of a street vendor or door-to-door sales worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic solution capable of this physical task would require expensive custom hardware and integration, far exceeding the cost of a low-wage human vendor performing the task manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product exists that autonomously stocks vendor carts or stands. This remains a domain requiring human physical labor with no mature production system alternative available today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or commercial product autonomously stocks street vendor carts today; this remains firmly in the physical/manual labor domain untouched by AI products. |
Distribute product samples or literature that details products or services.
14CI 5–24 · exposure 8 · augmentation 25 · importance 4.0/5 · click for rater detail
Distribute product samples or literature that details products or services.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Door-to-door sales and street vending occur primarily in low-digitization, physical, distributed sectors with small operators. These sectors show minimal AI adoption; the work remains largely manual and resistant to technology substitution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door sales and street vending are low-digitization, physically embedded occupations with minimal AI adoption or investment in this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with route planning or inventory tracking, but offers limited help for the core task of engaging customers and distributing materials, which depends almost entirely on human presence and interpersonal judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help design or personalize the literature/samples’ content and target lists beforehand, but offers little assistance during the actual distribution act itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Distributing physical samples or literature requires door-to-door navigation, interpersonal contact, and judgment about who to approach. Current AI systems cannot move independently in physical space or perform the human-contact aspects of this task end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | The physical act of walking door-to-door or on the street handing out samples/literature requires embodied mobility that current AI and robotics cannot perform reliably or cheaply; only the content-creation portion (designing literature) is automatable.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: trespassing laws limit who can access private properties, liability concerns over unsolicited contact, local regulations on street vending and distribution, and strong customer preference for human interaction in sales contexts create legal and organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing is required, but the task requires physical presence, mobility, and human interaction that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires physical labor and logistics in distributed locations. AI systems cannot replicate the cost-effectiveness of a low-wage worker walking neighborhoods; robotics capable of this are prohibitively expensive and unreliable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical distribution, so any 'AI' attempt (e.g., delivery robots) is far costlier and less flexible than a low-wage human doing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system today can independently perform door-to-door distribution of physical materials. This task inherently requires physical presence and real-world navigation that autonomous systems do not yet reliably accomplish at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotic product autonomously distributes physical samples or flyers door-to-door at scale; this remains a purely human physical task. |
Deliver merchandise and collect payment.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Deliver merchandise and collect payment.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Door-to-door sales occurs predominantly in small firms, informal sectors, and low-digitization environments. Adoption of automation is negligible; the sector remains labor-intensive and resistant to systematic AI deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Door-to-door sales is a low-digitization, small-scale, physically embedded occupation with minimal AI adoption or investment relative to office-based sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential exists; AI could assist with route planning or customer database lookup, but cannot assist the core tasks of in-person negotiation, cash handling, or physical delivery. The task is inherently human-centric. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with route planning, inventory tracking, or digital payment processing tools, but it offers limited assistance to the core physical delivery and cash collection activity. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence at customer locations, handling cash/payment methods, and interpersonal negotiation—capabilities far beyond current AI systems. No meaningful part of the full end-to-end process (delivery, collection, dispute resolution) can be automated at scale today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring travel, carrying goods, and in-person transactions; no current AI system can perform physical delivery or handle cash/payment collection autonomously. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: customers typically expect human interaction for sales and trust; regulatory restrictions on payment handling; liability for damage or theft; and significant organizational friction around autonomous systems on private property. Human contact is functionally required for this role. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but the physical nature of movement, cash handling, and personal customer interaction creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human cost of a door-to-door sales worker (wages, benefits, training) is substantially lower than the capital and operational cost of deploying autonomous delivery robots with manipulation and payment-handling capabilities, all-in. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-only solution for this physical task, so any comparison would require robotics and human oversight that would be far more expensive than a human vendor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs door-to-door sales, merchandise delivery, and payment collection. The task requires mobile robotics, autonomous navigation, physical handling, and real-time human interaction at a level not demonstrated in production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs door-to-door physical delivery and payment collection; this remains firmly in the domain of human labor or specialized robotics/logistics that are not general-purpose. |
Related occupations — Sales & Related
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.