Driver/Sales Workers
53-3031.00Median wage $38,770/yr409,180 employed (US)Rank #253 of 923 scored · top 27% by substitution
Drive truck or other vehicle over established routes or within an established territory and sell or deliver goods, such as food products, including restaurant take-out items, or pick up or deliver items such as commercial laundry. May also take orders, collect payment, or stock merchandise at point of delivery.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution36
Exposure30
Augmentation47
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
11 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
9%
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%31
panel mean rating 2.2/5 → substitution pressure 31/100
Technical feasibility todayw 20%29
panel mean rating 2.1/5 → substitution pressure 29/100
Cost vs. human wagew 15%30
panel mean rating 2.2/5 → substitution pressure 30/100
Adoption barriersw 20%inverted — strong barriers lower the score63
panel mean rating 2.5/5 (barrier strength) → substitution pressure 63/100
Sector adoption velocityw 10%29
panel mean rating 2.2/5 → substitution pressure 29/100
Task breakdown (11 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.
Record sales or delivery information on daily sales or delivery record.
82CI 75–89 · exposure 80 · augmentation 75 · importance 4.0/5 · click for rater detail
Record sales or delivery information on daily sales or delivery record.
82| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Logistics, delivery, and field sales sectors are already adopting mobile-first and AI-assisted record-keeping at scale (Amazon, UPS, DoorDash ecosystems). This is a mature adoption curve in digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Delivery and logistics sectors have widely adopted digital tracking and automated record systems, driven by e-commerce and fleet management demands, though some small operators still use paper. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered mobile forms, voice transcription, and auto-fill recommendations materially reduce driver time on paperwork while keeping the driver in control of accuracy and final submission. This is a strong augmentation use case already deployed. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where full automation isn't used, apps and AI-assisted data entry (auto-fill, voice input, error-checking) significantly speed up and improve accuracy of manual record keeping. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording structured sales and delivery information can be substantially automated via OCR, form extraction, voice-to-text, or direct integration with logistics/POS systems. Current AI systems can capture destination, quantities, customer names, and timestamps with high accuracy, achieving significant time savings compared to manual entry. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording sales/delivery data is a structured data entry task easily handled by mobile apps, scanners, and voice-to-text systems with automatic logging, meeting the time-saving threshold for most of the task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist for automating record capture; no license or human sign-off is required. Main friction is organizational integration and driver adoption of new tools, but these are operational, not legal. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, liability, or regulatory requirement mandating manual human record-keeping for sales/delivery logs; digital systems are already standard. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-based mobile capture (OCR, voice-to-text, automatic form submission) costs pennies per transaction versus 5–15 minutes of human wage time to record each delivery manually. The cost advantage is substantial once infrastructure is in place. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated logging via barcode scanners or app-based systems costs a fraction of a cent per transaction compared to manual paperwork time billed at driver wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (logistics software, CRM systems with mobile OCR, delivery apps) already perform data capture and record-keeping at scale in production. Form filling and voice recording are mature, though integration quality varies by implementation specifics. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Route accounting software, POS-linked delivery apps, and fleet management systems (e.g., handheld scanners, telematics) already automate this recording function reliably in production across delivery industries. |
Write customer orders and sales contracts according to company guidelines.
59CI 51–67 · exposure 58 · augmentation 75 · importance 3.7/5 · click for rater detail
Write customer orders and sales contracts according to company guidelines.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is uneven: large enterprises with sophisticated CRM and contract-management systems deploy automated drafting; small and mid-sized sales organizations remain largely manual. Pilots are common but deep production integration lags in driver/sales worker segments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This role is physical/field-based (driver-sales), a sector with generally slower AI adoption compared to office-based sales or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can assist significantly by auto-populating customer details, suggesting contract clauses, and flagging missing information, substantially speeding order and contract preparation while the human retains control and legal accountability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting of orders and contracts via templates, auto-fill, and voice-to-text while the driver/sales worker retains customer interaction and final approval. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can draft order forms and contracts by extracting customer details and applying templates, but typically requires human review and sign-off for legal compliance, customer-specific terms, and edge cases. This covers roughly half the task's time with meaningful setup. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting structured customer orders and sales contracts from templates/company guidelines is a text-generation and data-entry task that current AI (with order details as input) can largely automate, provided integration with order/CRM systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Legal and liability concerns around contract accuracy, customer consent requirements, and company policy compliance create meaningful friction, though no strict licensing requirement prevents automation. Organizational preference for human sign-off on contracts adds adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some contracts may require signatures, compliance with company-specific legal language, or customer relationship handling, but writing/drafting itself is not typically subject to licensing requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and integration costs are low compared to the loaded wage of a driver/sales worker performing this clerical task; the main variable is oversight overhead, which is modest for routine orders. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated order and contract generation via software/AI is far cheaper per transaction than having a human manually draft each document, especially at scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document-generation and form-filling tools exist in production (e.g., DocuSign integrations, CRM-embedded drafting), but error rates remain material for complex or non-standard orders, and most deployments still rely on human-guided templates rather than fully autonomous drafting. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Order-entry and contract-generation tools exist in sales/CRM software (e.g., automated order forms, contract templates with AI-assisted fill-in), but fully autonomous contract writing without human review is not yet standard for driver/sales roles specifically. |
Inform regular customers of new products or services and price changes.
55CI 44–66 · exposure 42 · augmentation 75 · importance 4.1/5 · click for rater detail
Inform regular customers of new products or services and price changes.
55| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Sales and customer service sectors are rapidly adopting automated notification systems and CRM-integrated messaging, with widespread production deployment in retail, automotive, and field sales industries. Measurement of displacement is clear and adoption is accelerating. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Driver/sales roles are in a physically-oriented, less digitized sector where AI-driven customer communication tools see slower, shallower adoption compared to office/professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-drafted product recommendations and price change messages augment sales workers by reducing time spent on routine notifications, allowing focus on relationship-building and high-value customer interactions. The human can review and personalize AI-generated content efficiently. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-generated talking points, automated reminders, and CRM-driven customer insights can meaningfully help drivers know what to communicate and when, improving their sales conversations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate personalized product messages and price change notifications automatically, the task requires nuanced judgment about customer relationships, timing, and tone that current systems struggle with. The human touch and relationship maintenance aspect of informing 'regular customers' resists full automation without significant human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate personalized notifications, emails, or texts about new products and price changes, but the delivery within an in-person driver-sales route context (relationship-based, face-to-face) limits full end-to-end automation.", |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory or licensing barriers to automating customer notifications. Some organizations prefer human contact for relationship reasons, but this is preference rather than legal requirement, and the barrier to adoption is modest. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, legal, or safety requirement mandating a human to convey pricing or product information to customers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated email or SMS notification systems cost a fraction of paying a driver/sales worker to make individual customer calls or visits. Once integrated into existing CRM infrastructure, the per-message cost is negligible compared to loaded labor costs. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated messaging is cheap, but the driver still performs the core route/delivery job, so cost savings apply only to the informational sliver of the task, not the whole role. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | CRM systems and marketing automation platforms can send templated or AI-generated notifications about products and price changes, but deployed systems often produce generic, low-engagement outputs. Real-world use shows moderate error rates in personalization and customer context awareness. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CRM and marketing automation tools reliably send personalized product/price updates today, but the driver-sales role's in-person, relationship-driven communication is not replicated by deployed AI products. |
Arrange merchandise and sales promotion displays or issue sales promotion materials to customers.
41CI 16–66 · exposure 49 · augmentation 50 · importance 4.2/5 · click for rater detail
Arrange merchandise and sales promotion displays or issue sales promotion materials to customers.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Driver-sales and field merchandising sectors (grocery, beverage, c-store) remain largely non-digital and labor-dependent, with adoption of automation lagging far behind office and finance sectors. Pilots exist but production deployment of autonomous agents for store-level promotional work is minimal and geographically sparse. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Route sales and merchandising work in retail/wholesale distribution sectors show slow AI adoption for physical tasks, though back-office planning tools are increasingly used. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Route optimization and inventory tracking AI can assist drivers in prioritizing store visits and material allocation; AR/mobile tools can guide display layout. These improve productivity, but the human driver remains central to execution and customer relationship management, limiting the scope and depth of augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help plan optimal display layouts, generate promotional content, and suggest which materials to distribute based on sales data, augmenting the human's decision-making even though execution remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Both merchandise arrangement and issuance of promotional materials are highly structured, repetitive physical and informational tasks. For promotional material distribution, AI-driven systems (robots or autonomous agents) can reliably pick, sort, and deliver materials; for display arrangement, vision-guided robotics can execute predefined layouts. Both achieve substantial time savings with comparable or improved quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical arrangement of merchandise and in-person distribution of promotional materials requires manual dexterity and mobility that current AI/robotics cannot perform end-to-end; only planning aspects (layout suggestions) are automatable.rifice |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Customer preference for human interaction, lack of regulatory prohibition but strong organizational inertia, and the requirement for real-time responsiveness to store-specific conditions create meaningful friction. Liability for misrepresented products and the expectation of personalized sales engagement in many retail settings provide substantial adoption barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but the task requires physical presence, dexterity, and customer interaction that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current robotic systems, their integration, maintenance, and oversight infrastructure are expensive relative to low-wage driver-sales workers. While cloud-based promotional material routing shows promise, the capital and operational costs of physical automation for display arrangement and material handling currently exceed or only marginally undercut human labor in many contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so the human remains the only option and cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Robotic systems exist for material handling and display in warehouses and retail (e.g., inventory robots, automated fulfillment), but real-world deployment for dynamic in-store display arrangement and customer-facing promotional distribution remains limited and narrow in scope. Products are emerging but lack the reliability and flexibility of mature solutions across varied retail environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously arranges physical retail displays or hands out promotional materials to customers in the field; this remains a manual task performed by the driver. |
Collect money from customers, make change, and record transactions on customer receipts.
40CI 30–50 · exposure 34 · augmentation 63 · importance 4.0/5 · click for rater detail
Collect money from customers, make change, and record transactions on customer receipts.
40| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Retail and service sectors have rapidly adopted digital POS and mobile payment systems; driver/sales roles in field service (HVAC, home services, utilities) increasingly use tablet-based payment capture and cloud-synced receipts, reflecting strong adoption momentum. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Route sales and delivery driving is a low-digitization, physical-labor-heavy sector where AI/automation adoption for this specific subtask remains limited to point-of-sale software rather than full task automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Digital payment systems, mobile POS apps, and real-time receipt generation substantially augment a driver/sales worker by eliminating manual arithmetic, reducing paper handling, and instantly recording transactions—raising transaction speed and accuracy while the worker retains customer interaction. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Mobile card readers, POS apps, and automated receipt generation meaningfully speed up the recording and payment portion of this task, improving accuracy and efficiency for the driver. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While payment processing and transaction recording can be partially automated via POS systems, collecting money from customers in person—which may involve cash handling, negotiating terms, or addressing payment disputes—requires human presence and judgment. Current AI cannot reliably handle the full sequence including adaptive responses to customer objections or payment complications. |
| Task automatability | claude-sonnet-5 | 2/5 | Payment collection and change-making are physical, in-person cash-handling activities tied to a delivery/sales route, which current AI cannot perform end-to-end; only the receipt/record-keeping portion is automatable.9 Overall time savings fall well short of the 50% threshold when the full task bundle is considered. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Payment processing involves regulatory compliance (PCI-DSS, tax recording) and customer expectations for human interaction, but no strict licensing requirement prevents automation of the mechanical parts. Cash handling and fraud prevention add friction without creating hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but physical presence for cash handling and customer interaction creates practical friction against remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | POS hardware and payment processing infrastructure have significant upfront and per-transaction costs; total cost of ownership including integration and support often approaches or exceeds the wage cost of a part-time driver/sales worker performing basic transactions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Digital payment/POS systems are cheap per transaction, but they don't replace the human driver who must still be physically present to deliver goods and handle cash, so overall cost savings versus the human worker are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | POS systems and automated payment processors (card readers, digital wallets, receipt printers) are mature and widely deployed in retail and service settings. However, the human-facing interaction component and cash handling still typically require a person, so end-to-end automation without human involvement remains uncommon. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Mobile POS and payment apps reliably automate transaction recording and digital payment processing, but physical cash collection and making change still require a human present at the point of sale. |
Review lists of dealers, customers, or station drops and load trucks.
36CI 35–38 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail
Review lists of dealers, customers, or station drops and load trucks.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Some larger logistics and retail distribution centers have begun adopting automated picking and loading, but adoption is still mostly in pilot or early production phases; small and mid-sized operators continue with manual loading. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Trucking/delivery is a physically-oriented, lower-digitization sector where AI adoption for physical tasks is slow, though route planning software is common. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by optimizing load sequences, route planning, and inventory tracking to guide workers, and digital pick lists reduce errors; however, the core physical loading task remains human-dependent and AI support is supplementary rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based route optimization and inventory/list management tools can meaningfully assist drivers in organizing drops and load order even though physical loading remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Loading trucks is primarily a physical manipulation task that current robotic systems struggle with in unstructured environments; reviewing lists can be automated but the actual physical loading and real-world problem-solving (e.g., spatial packing, weight distribution) cannot be reliably automated today at production scale. |
| Task automatability | claude-sonnet-5 | 2/5 | Reviewing delivery lists can be aided by software, but physically loading trucks and sequencing loads based on judgment about routes and space remains largely manual today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers, physical safety, logistics integration requirements, and the need for real-time adaptive decision-making in varied warehouse conditions create moderate friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this sub-task, though safety and liability concerns around loading and vehicle handling create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic loading systems are capital-intensive and require significant infrastructure; compared to the relatively low loaded wage of a driver/sales worker, the amortized cost of automation remains unfavorable for most operators. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software for list review is cheap, but robotic loading is expensive and immature, so total automation cost is not clearly cheaper than a driver's time for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While some warehouses use automated picking systems and robotic arms for structured loads, the flexible real-world task of reviewing route lists and dynamically loading trucks with variable inventory remains heavily manual; no mainstream product reliably does the full task end-to-end. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Route/load-planning software exists and is used in logistics, but the physical loading and final verification is still done by humans; no deployed product fully performs this combined task. |
Listen to and resolve customers' complaints regarding products or services.
33CI 30–35 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Listen to and resolve customers' complaints regarding products or services.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Driver/sales worker sectors tend to be smaller firms and traditional organizations with lower digitization levels. While some large retailers have piloted AI complaint systems, adoption remains limited and production deployment is rare in the broader driver/sales workforce. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Driver/sales roles are in a physically-oriented, lower-digitization sector where AI adoption for direct customer interaction remains nascent compared to office-based customer service functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by summarizing complaints, suggesting relevant policies or solutions, and flagging patterns, meaningfully improving a human agent's productivity. However, the human must remain central to judgment calls and final resolution, making augmentation helpful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (e.g., mobile apps with suggested responses, complaint logging, sentiment analysis, or automated follow-up) can help a driver/sales worker track and address complaints more efficiently, though the core interpersonal resolution remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can parse complaint text and suggest resolutions, resolving complaints requires empathy, negotiation, and contextual judgment about when to escalate or make exceptions. Current systems cannot reliably handle the nuanced emotional and interpersonal aspects that define successful complaint resolution, and the task cannot meet a 50% time-saving threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | While AI chatbots can handle scripted complaint resolution, this task in the context of a driver/sales worker involves in-person, relational handling of complaints often tied to delivery issues, product freshness, or route-specific problems that require real-time judgment and rapport, limiting automatable time savings to a small fraction of the task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict legal requirements mandating human complaint resolution, customer satisfaction and trust concerns create practical friction against full automation. Many organizations and customers prefer human contact for complaint handling, and organizational inertia slows adoption of pure AI solutions. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but customer preference for a known, accountable human on their delivery route and the relational/trust nature of the interaction create moderate organizational friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems for complaint handling require significant integration, training, and ongoing human oversight to prevent costly errors. Combined with the salary of driver/sales workers typically being moderate, the all-in cost of AI solutions remains comparable to or exceeds the human alternative when factoring in oversight and liability. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | An AI system could cheaply triage some complaints, but since this task is embedded in a physical delivery/sales role, replacing the human interaction still requires the person on-site, so cost savings from AI alone are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and automated systems exist for initial complaint triage, but deployed products still have high error rates in understanding complex complaints and determining appropriate resolutions. Real-world complaint resolution requires judgment calls that AI cannot reliably execute at production scale without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed chatbots and call-center AI handle routine complaint intake in many industries, but there is no mature product that stands in for a driver/sales worker resolving in-person, on-route customer complaints reliably today. |
Maintain trucks and food-dispensing equipment and clean inside of machines that dispense food or beverages.
17CI 10–24 · exposure 8 · augmentation 13 · importance 4.0/5 · click for rater detail
Maintain trucks and food-dispensing equipment and clean inside of machines that dispense food or beverages.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Driver/sales work is typically performed in small independent or franchise operations with low capital budgets and high physical variability, sectors that historically lag in AI adoption. The distributed, on-location nature of maintenance work and lack of standardized environments slow deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Driver/sales roles involve physical, low-digitization work with minimal AI or robotics adoption for maintenance and cleaning tasks in this sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance through diagnostic tools, route optimization for maintenance visits, or equipment status alerts, but the core physical tasks of maintenance and cleaning offer limited scope for human-AI collaboration, as the human cannot easily delegate mid-task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers little to no meaningful assistance for manual cleaning or mechanical maintenance tasks of this nature. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical maintenance and cleaning of dispersed equipment requires manipulation in diverse, unstructured environments. While some inspection and diagnostics could be partially automated with vision systems, hands-on repair, parts replacement, and interior cleaning of machines remain heavily dependent on physical dexterity and real-time problem-solving that current robotics cannot reliably achieve at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical maintenance and cleaning task requiring manual dexterity and mobility inside vehicles/equipment, which current AI systems cannot perform.rd |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There is some friction from the need for physical presence, equipment-specific knowledge, and customer interaction, but no hard legal licensing requirements prevent automation. Organizational adoption would face mainly logistical and economic barriers rather than regulatory ones. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but the physical, unstructured nature of the environment (trucks, food equipment) creates practical friction against automation beyond regulatory issues. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of performing even portions of this work (maintenance, repair, cleaning) cost significantly more than the hourly wages of driver/sales workers, and require extensive setup and oversight, making deployment economically unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any AI-based approach (e.g., robotics) would be far more costly than a human worker performing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production systems today reliably perform general truck maintenance, food-dispensing equipment repair, or interior machine cleaning end-to-end. Robotic systems exist for narrow specialized tasks but lack the adaptability and dexterity required for the diverse maintenance scenarios described. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical truck maintenance or equipment cleaning; this remains firmly in the domain of human manual labor. |
Collect coins from vending machines, refill machines, and remove aged merchandise.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.1/5 · click for rater detail
Collect coins from vending machines, refill machines, and remove aged merchandise.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in low-digitization sectors (retail, food service field operations) with fragmented, small-scale machine networks. Adoption of automation for vending machine servicing remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Vending/route sales is a low-digitization, physical-labor sector with minimal AI agent adoption for these hands-on logistics tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a human performing coin collection and merchandise refilling; the task is purely manual and physical, with no decision-support or cognitive component that AI could enhance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with route optimization, inventory prediction, or scheduling, but offers no direct help with the physical acts of collecting coins or restocking machines. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in unstructured environments (collecting coins, refilling machines, removing merchandise), which current AI systems cannot perform end-to-end. Robotics for this application remain research-stage and lack the dexterity and adaptability needed for reliable deployment. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring driving, manual coin collection, restocking, and inventory removal at multiple sites—no current AI system can perform these physical manipulations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical access to vending machines, property control, and cash handling create minor friction, but no hard licensing or regulatory barriers prevent automation attempts. The main barrier is technical infeasibility rather than legal. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the task requires physical presence, vehicle operation, and handling cash/inventory, creating practical (not legal) barriers to remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robots, maintenance, and integration far exceeds the low wage cost of human drivers/sales workers performing these routine physical tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for physical restocking and cash collection, so AI cost is not comparable; the human remains the only cost-effective option today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full suite of coin collection, machine refilling, and merchandise removal at scale. Physical automation in vending contexts remains too narrow and context-specific for production deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical vending machine servicing; this remains entirely a human/robotic-hardware task not addressed by AI software products. |
Drive trucks to deliver such items as food, medical supplies, or newspapers.
13CI 5–20 · exposure 8 · augmentation 38 · importance 4.3/5 · click for rater detail
Drive trucks to deliver such items as food, medical supplies, or newspapers.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains pilot-stage with limited production deployment; most logistics companies continue to rely on human drivers, though some autonomous shuttle projects exist in controlled environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Trucking and delivery sectors have very low AI/autonomy adoption in production; physical, unstructured driving environments make this a laggard sector for automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Route optimization, real-time traffic navigation, and delivery coordination tools assist drivers meaningfully, raising efficiency and reducing fatigue, though the human remains central to safe operation and customer delivery. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with route optimization, dispatching, and navigation, but does not meaningfully augment the core physical driving and delivery task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While autonomous vehicles are in testing, end-to-end autonomous delivery with 50% time savings and equal safety/quality is not reliably deployed at scale today. Last-mile navigation, customer interaction, and liability remain unresolved for general deployment. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical driving and delivery of goods to multiple stops requires real-world navigation, loading/unloading, and customer interaction that current AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Heavy regulatory oversight of commercial vehicle operation, liability requirements, insurance frameworks, and customer preference for human interaction create substantial legal and organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Commercial driving requires licensing (CDL for larger vehicles), liability for road safety is high, and regulations still require human oversight or presence in nearly all jurisdictions for last-mile delivery driving. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous vehicle infrastructure, insurance, and sensor maintenance remain expensive; human driver wages in many regions are still competitive when factoring in vehicle capital and integration costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous trucking systems require expensive sensor suites, safety monitoring infrastructure, and remain costlier than a human driver for typical route-based delivery work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No current production system reliably performs full truck delivery autonomously across diverse routes, weather, and customer interaction requirements. Pilot programs exist but lack the maturity and scale required for demonstrated reliability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | Autonomous delivery trucks remain in limited pilot programs (e.g., select highway freight corridors) and are not deployed reliably for general multi-stop delivery routes like food, medical supplies, or newspapers. |
Sell food specialties, such as sandwiches and beverages, to office workers and patrons of sports events.
7CI 5–10 · exposure 0 · augmentation 25 · click for rater detail
Sell food specialties, such as sandwiches and beverages, to office workers and patrons of sports events.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This occupation operates in traditional, low-digitization sectors (field sales, small independent operators, sports venues) with limited adoption of automation technology. Pilot projects are nearly absent in this space. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Mobile food/beverage vending and driver-sales roles are in a low-digitization, physical-labor sector with minimal AI or robotics adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route optimization, inventory tracking, or menu recommendations via a mobile app, but these are peripheral to the core task of interpersonal selling and customer engagement. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with route optimization, inventory tracking, or payment processing, but offers little direct help with the core physical selling and driving activities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires direct human-to-human sales interaction, relationship building, and responsive communication with customers in varied contexts (offices, sports events). Current AI cannot perform the full sales cycle—negotiation, persuasion, handling objections, and closing transactions—with the interpersonal presence required in mobile, face-to-face settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physically driving a vehicle, transporting food, and conducting in-person cash/card sales interactions—none of which current AI systems can perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task has strong barriers to automation: customers expect human interaction and relationship, direct payment and liability responsibilities, local health and food handling regulations, and the requirement for a human to physically operate the vehicle and manage transactions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically protects this role, but physical presence, cash handling, and direct customer interaction create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of mobile sales would require substantial infrastructure (robotics, payment systems, inventory management, real-time navigation) with high operational costs, far exceeding the wage of a human driver/sales worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical driving, handling, and selling involved, so any AI-based approach would require robotics far more costly than a human worker today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed system performs end-to-end food specialty sales to office workers and sports patrons. Autonomous systems cannot navigate physical sales environments, manage inventory, handle cash/payments, or engage in natural persuasive dialogue at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product drives vehicles, handles physical food goods, and completes in-person sales transactions to the public; this remains firmly outside current product capability. |
Related occupations — Transportation & Material Moving
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.