Coin, Vending, and Amusement Machine Servicers and Repairers
49-9091.00Median wage $47,450/yr26,410 employed (US)Rank #501 of 923 scored · top 54% by substitution
Install, service, adjust, or repair coin, vending, or amusement machines including video games, juke boxes, pinball machines, or slot machines.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution27
Exposure21
Augmentation38
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
18 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%22
panel mean rating 1.9/5 → substitution pressure 22/100
Technical feasibility todayw 20%18
panel mean rating 1.7/5 → substitution pressure 18/100
Cost vs. human wagew 15%20
panel mean rating 1.8/5 → substitution pressure 20/100
Adoption barriersw 20%inverted — strong barriers lower the score57
panel mean rating 2.7/5 (barrier strength) → substitution pressure 57/100
Sector adoption velocityw 10%13
panel mean rating 1.5/5 → substitution pressure 13/100
Task breakdown (18 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.
Keep records of merchandise distributed and money collected.
77CI 72–81 · exposure 75 · augmentation 75 · importance 4.5/5 · click for rater detail
Keep records of merchandise distributed and money collected.
77| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Vending and amusement machine operators show moderate adoption of automated tracking systems, with pilots and mixed deployment common, but full production-scale displacement remains uneven across smaller operators and legacy equipment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Modern vending/amusement operators increasingly adopt telemetry and cashless systems, but the sector overall (small operators, legacy machines) still lags digitization compared to information/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered dashboards, reconciliation alerts, and anomaly detection substantially assist technicians in monitoring machine health and cash discrepancies, raising their ability to identify and prioritize service calls. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where full automation isn't in place, AI-enabled tracking apps and automated ledgers substantially reduce manual recording effort and error for the technician. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Record-keeping of merchandise distribution and money collection is highly structured data entry that can be automated end-to-end via barcode scanning, IoT sensors on machines, and payment system APIs. Current AI systems can integrate these data streams, reconcile discrepancies, and generate logs with minimal human intervention, easily exceeding 50% time savings. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording merchandise distributed and money collected is a structured data-entry/logging task that off-the-shelf software, POS integrations, and telemetry systems can largely automate, especially when machines have digital payment/tracking systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or regulatory requirement mandates human record-keeping for vending machines; primary friction is organizational inertia and preference for audit trails, but these do not legally prevent automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing, legal, or human-contact requirements for recordkeeping of inventory and cash; it's a purely administrative task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated record-keeping via integrated systems costs a fraction of manual data entry labor; infrastructure investment amortizes across many machines, making per-transaction cost orders of magnitude cheaper than human clerical work. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated telemetry and cloud-based tracking systems cost very little per transaction compared to a technician's time spent manually recording data. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed inventory management and point-of-sale systems already automate transaction logging at scale in retail and vending contexts. While edge cases (manual cash counts, damaged records) require oversight, the core task of logging distributed merchandise and collected money is reliably performed by mature products in production. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Vending and amusement machine management software with automated inventory and cash tracking is already deployed widely in production, though older cash-only machines still require manual logging. |
Maintain records of machine maintenance and repair.
74CI 70–77 · exposure 75 · augmentation 75 · importance 4.0/5 · click for rater detail
Maintain records of machine maintenance and repair.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Field service and equipment maintenance sectors show moderate adoption of digital record systems and AI-assisted logging, but uptake remains uneven—larger chains and fleet operators lead, while independent repair shops lag. Pilots are common; production automation of record-keeping is less ubiquitous than in white-collar administrative roles. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Vending/amusement machine servicing is a small, physically dispersed, low-digitization sector where broader field-service software adoption lags behind information/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist technicians by auto-populating repair forms from photos or voice notes, flagging relevant historical records, and organizing data for faster decision-making. This keeps the technician in the loop while substantially reducing time spent on documentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled mobile apps and dictation tools significantly speed up record-keeping for technicians while they remain in control of the actual repair work. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Record maintenance and repair documentation is primarily structured data entry and organization that AI can automate end-to-end using OCR, form processing, and database integration. Current systems can extract handwritten or printed notes, classify repair types, and log events with high accuracy, easily achieving 50% time savings over manual entry. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording maintenance and repair activities is largely structured data entry that current AI/digital systems (voice-to-text, form-filling, CMMS integrations) can handle with high time savings.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates a human maintain these records; regulatory frameworks typically require records to exist but not who creates them. Minor friction may come from organizational legacy systems or technician preference for manual workflows, but no hard blockers exist. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human personally maintain these records; software-based logging is already standard practice. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Document processing and record automation via RPA or APIs cost pennies per transaction, vastly cheaper than paying a technician to manually write, transcribe, or organize maintenance logs. The all-in cost is well below human wage for equivalent output. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated logging via mobile apps/voice input costs a fraction of a technician's time spent manually writing records, giving a large cost advantage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (RPA platforms, document processing APIs, service management software with AI integration) reliably handle maintenance record creation and logging in production environments across multiple industries. While some complex free-text summaries may need human review, core record maintenance is mature and widely implemented. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature field-service and CMMS products (ServiceTitan, Fiix, UpKeep) already digitize and auto-log maintenance records reliably in production today. |
Record transaction information on forms or logs, and notify designated personnel of discrepancies.
71CI 65–77 · exposure 70 · augmentation 63 · importance 3.8/5 · click for rater detail
Record transaction information on forms or logs, and notify designated personnel of discrepancies.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Vending and gaming machine operators have moderate digital maturity; while larger chains increasingly deploy automated monitoring systems, the sector is fragmented with many smaller operators still using manual logs, limiting rapid sector-wide adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This occupation involves physical, field-based work with lower digitization rates, and adoption of automated reporting/telemetry is proceeding slowly compared to office-based sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by auto-populating forms, highlighting discrepancies, and generating alerts, materially reducing the technician's data-entry burden and error-checking time while they retain oversight and decision-making on discrepancy resolution. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled logging apps, OCR for forms, and automated discrepancy flagging can significantly speed up and improve accuracy of this record-keeping task while the technician remains responsible for physical servicing. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording transaction data and identifying discrepancies can be largely automated with OCR, database matching, and rule-based logic to flag anomalies; only the notification and follow-up typically require human judgment, achieving well over 50% time savings for the data-processing portion. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording transaction data and flagging discrepancies is a structured data-entry and rule-based comparison task that AI/automation systems handle well, especially with digital forms or connected devices feeding data automatically.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Light barriers exist: some jurisdictions may require human sign-off on transaction records, and companies may prefer human verification for liability reasons, but neither is a hard legal requirement in most settings. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory requirement mandates a human record transactions or flag discrepancies; this is purely an administrative task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven logging and anomaly detection incurs minimal inference cost compared to a technician's hourly wage for manual record-keeping and comparison, likely making automation 5–10× more cost-effective all-in. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated logging and alerting systems (e.g., telemetry-enabled machines) cost far less per transaction record than manual logging and notification by a technician. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature solutions exist for transaction logging, automated discrepancy detection, and alerting in payment and vending industries; systems reliably perform this in production, though some integration with legacy equipment may require manual steps. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Many vending/amusement machines still use manual counting and paper logs, so while digital reporting systems exist and work reliably, broad deployment across this occupation is uneven. |
Order parts needed for machine repairs.
47CI 30–65 · exposure 45 · augmentation 63 · importance 3.9/5 · click for rater detail
Order parts needed for machine repairs.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Vending and amusement machine service is a fragmented, small-firm-dominated sector with low overall digitization. These businesses rely heavily on established relationships and manual processes, and adoption of AI-driven parts ordering remains minimal in production systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This occupation is in a physical, low-digitization trade sector where automation of back-office tasks like ordering lags behind information/professional service sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by generating candidate part lists, checking stock levels, or drafting orders for technician review, meaningfully reducing the manual effort of searching catalogs and composing orders while the human technician retains final authority over vendor selection and order placement. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted inventory and procurement tools can meaningfully speed up part identification and ordering, letting technicians focus on diagnosis and repair while software handles reordering logistics. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Ordering parts requires identifying which specific components are needed based on machine diagnosis and then placing orders. While AI could help generate part lists from diagnostic data, the task involves judgment about machine-specific compatibility, supplier availability, and operational context that currently requires significant human oversight and verification. |
| Task automatability | claude-sonnet-5 | 4/5 | Ordering parts is a structured, transactional task (identifying part numbers, checking inventory, placing purchase orders) that current AI/software systems can largely handle given integration with inventory and supplier systems.atable |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Supplier relationships, credit terms, and vendor qualifications often require human decision-making and authorization. However, parts ordering itself is not legally restricted, and some standardization of the process could reduce friction, though organizational systems and established vendor relationships provide moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement for a human to place parts orders; main friction is organizational integration with existing inventory/vendor systems and approval workflows. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems (LLMs, automation tools) have low inference costs, but the task requires integration with inventory systems, supplier databases, and approval workflows. Combined with necessary human oversight to prevent costly ordering mistakes, the all-in cost remains comparable to a technician simply placing the order themselves. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated procurement systems (e-procurement, EDI, AI-assisted inventory reordering) are cheap to run per transaction compared to a technician's time spent manually sourcing and ordering parts. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Existing AI systems can draft purchase orders or suggest parts based on text descriptions, but no widely deployed product reliably performs end-to-end parts ordering for specialized vending/amusement machines without human verification of part specifications, vendor selection, and supplier relationships. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Procurement automation and inventory management software exist and are used in many maintenance operations, but full autonomous parts-ordering tied to diagnostic triggers is less commonly deployed specifically for vending/amusement machine repair contexts. |
Refer to manuals and wiring diagrams to gather information needed to repair machines.
34CI 30–39 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail
Refer to manuals and wiring diagrams to gather information needed to repair machines.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Vending and amusement machine service is a traditional, geographically dispersed, small-firm industry with limited digitization and slow technology adoption; pilots of AI-assisted reference lookup are rare. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This occupation is in a low-digitization, physical trade sector with limited AI tool adoption; leading-edge digital manual search assistants are not yet common in this niche field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by rapidly searching manuals, cross-referencing multiple documents, and highlighting relevant sections, meaningfully accelerating the information-gathering phase while the technician makes final judgment calls about repair strategy. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can meaningfully assist technicians by quickly surfacing relevant manual sections or explaining wiring diagram symbols, saving search time even though the technician remains responsible for diagnosis and repair. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can retrieve and summarize manual information, the task requires contextual judgment about which manuals apply to a specific machine and how to interpret wiring diagrams in relation to actual physical hardware—something that demands human diagnostic reasoning and cannot independently save 50% time with equal quality today. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can retrieve and summarize manual content or wiring diagram information via multimodal models, but interpreting physical wiring diagrams alongside real machine inspection still requires human integration and physical verification.The information-gathering sub-step is automatable, but it's a small fraction of the full repair task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There are no regulatory or licensing barriers to automating information retrieval itself, though technicians must still interpret results and apply them to physical repair work, creating some organizational friction around trust and liability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically governs consulting manuals, though some regulatory/safety liability may apply if misinterpretation leads to faulty repairs; mostly organizational habit and low digitization of documentation are the barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Manual/diagram lookup and AI processing costs are modest, but the actual labor cost for a field technician is low per lookup task, and the system would still require human verification and judgment, making the all-in cost comparable to or slightly higher than direct human lookup. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | An AI-based document/diagram lookup tool is cheap to run compared to a technician's time spent searching manuals, but the technician still needs to be present for the physical repair, limiting overall savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | OCR and document retrieval systems exist, but current AI struggles with complex technical diagrams, cross-referencing multiple manuals, and handling proprietary or outdated documentation typical in the vending/amusement machine industry; no deployed product reliably performs this end-to-end. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some field-service AI assistants and chatbots exist that can search manuals and answer technical queries, but reliable, production-grade interpretation of wiring diagrams and integration into repair workflows is not yet mature or widespread. |
Collect coins and bills from machines, prepare invoices, and settle accounts with concessionaires.
26CI 23–30 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Collect coins and bills from machines, prepare invoices, and settle accounts with concessionaires.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in small-to-medium service businesses (vending, amusement) that have low digitization and resist automation adoption. The sector is not a leader in AI deployment; operational changes tend to lag broader technology trends. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Vending/amusement machine servicing is a low-digitization, physically dispersed trade sector with minimal reported AI agent deployment for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist meaningfully with invoice generation, account reconciliation, and route optimization, raising servicer productivity on the administrative side. However, the physical collection task itself offers limited augmentation opportunities. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered accounting and invoicing software can meaningfully speed up the settlement and invoice-preparation portion of the task, even though physical collection remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Coin and bill collection requires physical presence at distributed machines and handling of cash—fundamentally requiring human or specialized robots. While invoicing and account settlement are highly automatable, the physical collection component prevents end-to-end automation at ≥50% time savings with current AI/agent systems. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical coin/bill collection requires manual presence at machines; only the invoicing/accounting portion is digitizable, leaving the bulk of the task (physical collection, transport, cash handling) unautomatable with current AI. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: cash handling involves fiduciary responsibility and reconciliation requirements; concessionaires typically require human verification of collected amounts; and regulatory/contractual obligations often mandate human sign-off on financial settlements. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Cash handling involves accountability, security, and audit trail requirements plus concessionaire trust relationships, creating moderate organizational and liability friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI systems cannot replace the physical collection task, which dominates labor cost. Automating only the invoicing/settlement portions leaves the expensive collection work untouched, making the net cost ratio unfavorable compared to existing service models. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | The physical collection portion still requires human labor and transport, so overall cost savings from AI are limited to the bookkeeping/invoice generation slice, not the full task cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system reliably performs physical coin/bill collection from machines. Invoicing and accounting software exists but does not form an integrated, production-ready system for this specific workflow without manual data entry and cash handling oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Cash-handling software and automated accounting tools exist and are used for settlement bookkeeping, but no deployed product performs the physical coin/bill collection or full concessionaire settlement process end-to-end. |
Test machines to determine proper functioning.
23CI 14–31 · exposure 20 · augmentation 38 · importance 4.2/5 · click for rater detail
Test machines to determine proper functioning.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | The service industry for physical machines remains heavily fragmented across small, independent operators and regional service providers with lower digital infrastructure adoption; these are laggard sectors in AI integration compared to information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This is a physical, low-digitization repair trade with minimal AI adoption reported industry-wide. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted diagnostics could usefully augment technician testing by analyzing machine logs, predicting failure modes, or flagging anomalies, enabling faster root-cause identification; however, the human technician remains central to validation and hands-on troubleshooting, limiting the transformation potential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help via diagnostic checklists, fault-code lookup, or predictive maintenance data analysis, but it doesn't materially transform the hands-on testing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Testing machine functioning requires physical interaction with hardware, sensor diagnostics, and contextual troubleshooting that current AI cannot perform autonomously; while remote diagnostics or log analysis could partially automate testing workflows, the physical manipulation and real-time feedback loops necessary for comprehensive testing remain beyond current automation capabilities. |
| Task automatability | claude-sonnet-5 | 2/5 | Testing coin, vending, or amusement machines requires physical interaction—inserting coins, observing mechanical dispensing, checking sensors—that current AI cannot perform without robotic embodiment, which is not standard in this occupation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Coin, vending, and amusement machine servicing often involves customer-facing equipment access, liability for malfunctions, and regulatory compliance around payment systems and public safety; these factors create organizational and legal friction against full automation without human technician sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing typically required, but the physical nature of the task and liability for faulty machines create some practical friction against remote or automated testing. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Testing machines requires on-site physical access and specialized equipment diagnostics that AI systems cannot replicate cost-effectively; the current overhead of integrating AI-assisted diagnostics with human technician labor makes it more expensive than direct human testing. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Without a viable AI or robotic substitute, the human technician remains the only cost-effective option; deploying robotics for this narrow task would be far more expensive than labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end machine testing without human involvement; remote monitoring systems exist but require technician interpretation and manual testing of hardware components, keeping this task in the assisted-diagnostics category rather than autonomous execution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI products that physically test and diagnose vending or amusement machines; this remains a hands-on task performed by technicians. |
Contact other repair personnel or make arrangements for the removal of machines in cases where major repairs are required.
20CI 5–35 · exposure 13 · augmentation 38 · importance 3.7/5 · click for rater detail
Contact other repair personnel or make arrangements for the removal of machines in cases where major repairs are required.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The vending machine service sector is characterized by small local operators with low digital infrastructure, making it a laggard sector in AI adoption with minimal pilot deployment of autonomous coordination systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Field service and repair trades adopt AI communication tools slowly; this sector is low-digitization and physical-labor oriented, lagging behind information-sector adoption.' |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by drafting contact lists or suggesting removal vendors, but the actual communication and negotiation requires human judgment about technical requirements, liability, and relationship management, limiting meaningful productivity gains. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with drafting communications, scheduling, and tracking repair requests, improving efficiency of coordination even though the human retains decision authority.' |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time human coordination and negotiation with other service personnel, often involving scheduling, logistics, and judgment about repair severity. Current AI systems cannot reliably initiate contact, negotiate terms, or make binding arrangements without human intermediation. |
| Task automatability | claude-sonnet-5 | 2/5 | This is largely a coordination/communication task tied to physical machine logistics; AI could help draft messages or schedule but cannot perform the physical arrangement or judgment about removal end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves authorized communication on behalf of the organization, potential liability for incorrect technician assignment, and reliance on human judgment about repair severity and vendor relationships—all creating organizational and legal friction against pure automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical access, contractual dispatch procedures, and reliance on human technicians for judgment on repair severity create moderate friction.' |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of oversight required to ensure correct technician contact, scheduling accuracy, and proper arrangement authorization would exceed the minimal human wage cost of a technician making a phone call or email communication. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While messaging/scheduling assistance is cheap, the overall task still requires human dispatch coordination and physical logistics oversight, limiting cost savings from AI alone.' |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs independent coordination with external repair personnel or arranges removal logistics. This requires real-world negotiation, liability discussion, and organizational integration that exceeds current AI capability. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Scheduling and communication tools exist and are used broadly, but no deployed product autonomously manages the decision-making and coordination of physical equipment removal for this niche field.' |
Fill machines with products, ingredients, money, and other supplies.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.4/5 · click for rater detail
Fill machines with products, ingredients, money, and other supplies.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The vending and amusement machine service sector is small, fragmented, and low-digitization; adoption of advanced robotics is minimal and unlikely to accelerate soon given the capital constraints of service operators. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation is in a low-digitization, physical-labor sector with minimal AI/robotics adoption for stocking tasks currently in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route optimization and inventory tracking, but offers limited direct augmentation for the manual filling task itself; most value is in scheduling and planning rather than at-task support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with route optimization, inventory prediction, or scheduling around this task, but it doesn't meaningfully assist the physical act of filling machines itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robots could theoretically fill machines, this task requires physical access to diverse machine types, handling of cash/coins, and precise placement of supplies. Current AI lacks the embodied automation and dexterity needed to perform this end-to-end with 50% time savings at equal quality in the field. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring travel to machine locations, physical handling of stock, ingredients, and cash, which current AI systems cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no hard licensing barriers to automate this task, though some venues may prefer human oversight for security (handling cash) and customer interaction, creating modest friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, but physical access, security of cash handling, and route logistics create practical friction against remote or software-only substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Mobile manipulation systems capable of this task cost hundreds of thousands to millions of dollars, while a human servicer earns $30k–$50k annually; all-in automation cost remains far above human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute performing this physical task, so AI cost is effectively infinite or inapplicable compared to a human worker's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs this task autonomously today. Mobile manipulation robots exist in research and limited industrial settings, but none are in production for field refilling of vending, coin, or amusement machines. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical restocking of vending/amusement machines; this remains entirely a human/robotic-manual task with no robotics products deployed at scale for this specific job. |
Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams.
17CI 10–24 · exposure 8 · augmentation 25 · importance 3.8/5 · click for rater detail
Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in small, dispersed service operations with low digitization (vending machines, arcades). Adoption of AI tooling in this sector is minimal; technicians remain predominantly hands-on field workers with little institutional pressure for automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation is physical, field-based, and low-digitization; AI adoption in mechanical repair trades remains minimal with no meaningful displacement trend. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist by analyzing machine images for fault diagnosis or providing circuit diagram interpretation, but augmentation is limited because the technician must still perform all physical work. Diagnostic aids exist but offer incremental rather than transformative productivity gains. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostic lookup, wiring diagrams, or troubleshooting guides, offering modest assistance, but it does not meaningfully change the hands-on repair workflow itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could potentially assist with diagnostics via machine vision or circuit analysis, the task fundamentally requires hands-on mechanical and electrical work (soldering, part replacement, physical adjustment) that current AI systems cannot perform end-to-end. AI tools could support diagnosis but cannot execute the core repair work at meaningful time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is hands-on physical diagnosis, disassembly, soldering, and mechanical repair work in the field, which current AI systems cannot perform end-to-end; no robotics system today can substitute for a technician's physical manipulation and troubleshooting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical safety, liability for improper repairs, and lack of mature automation technology create moderate friction, but there are no formal licensing requirements specifically prohibiting AI-assisted repair. Organizational risk around equipment damage and customer dissatisfaction provide some protection. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing typically required, but the physical nature of the work (tools, electrical/mechanical components, on-site access) creates practical barriers to automation, along with liability for faulty repairs. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI systems have no cost advantage because they cannot perform the task itself. Inspection robots or diagnostic aids may exist, but they require human technicians to execute repairs, so all-in cost remains higher than the human wage for actual repair work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical repair task, so the human technician remains the only cost-effective option; any AI assistance is a minor add-on, not a replacement of labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical machine repair, adjustment, or soldering end-to-end. Vision-based diagnostics exist in research, but production systems that autonomously service vending machines or repair electrical components do not exist at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical repair of coin/vending/amusement machines; at most AI could provide diagnostic guidance via manuals, but actual repair execution remains fully human. |
Clean and oil machine parts.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Clean and oil machine parts.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Coin and amusement machine service is a low-digitization, geographically distributed sector with small operators. Adoption of advanced automation in field maintenance remains minimal, and the sector has traditionally lagged in automation investment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation involves low-digitization, physical field service work with minimal AI or robotics adoption reported in this sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance for physical cleaning and oiling tasks; there is no meaningful augmentation channel for guidance, diagnostics, or decision support that would improve a technician's performance at this specific manual work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help schedule maintenance, log service history, or diagnose issues via sensor data, but offers little direct assistance with the physical cleaning and oiling itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning and oiling machine parts requires physical manipulation in unstructured environments with variability in part geometry, access, and contamination levels. Current AI lacks embodied robotics capable of reliably performing this sustained manual work without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hands-on cleaning and lubrication of mechanical parts; no current AI system can perform physical manipulation without embodiment via robotics, which is not deployed for this niche task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers to automating this maintenance task, the need for on-site physical manipulation in customer locations and the low labor cost creates organizational friction and customer preference for human technicians. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, but physical access to machines in varied field locations and mechanical dexterity needs create practical barriers to any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics with the dexterity and sensing required to clean and oil parts safely would be capital-intensive and expensive to deploy and maintain relative to low-wage manual labor in this domain. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute at any cost for this specific physical maintenance task, so the human remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system today reliably cleans and oils diverse machine parts autonomously in field conditions. This is a physical task requiring dexterity and sensorimotor adaptation that remains at research or prototype stage for general scenarios. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product exists that autonomously cleans and oils vending, coin, or amusement machine parts; this remains firmly in the domain of human technicians. |
Disassemble and assemble machines, according to specifications and using hand and power tools.
13CI 10–15 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Disassemble and assemble machines, according to specifications and using hand and power tools.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Machine servicing is a physical, location-based trade with low digital infrastructure and small firm dominance; adoption of AI or robotics remains minimal, reflecting laggard sector characteristics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This is a physical, low-digitization repair trade with minimal AI/robotics adoption in the field currently. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to technicians performing hands-on disassembly and assembly work; the task is inherently manual and does not benefit from language models, vision systems, or agent capabilities available today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with retrieving specifications, diagnostic guidance, or repair manuals via AR/chat tools, but offers little direct help with the physical assembly/disassembly steps themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Disassembling and assembling physical machines requires dexterous manipulation in varied spatial configurations, real-time problem-solving around stuck or misaligned parts, and adaptation to specific machine variants. Current AI systems cannot reliably operate physical tools or adapt to unexpected mechanical challenges without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical disassembly/assembly of mechanical machines requires manual dexterity, spatial reasoning, and tool manipulation that current AI systems cannot perform end-to-end without embodied robotics far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no explicit licensing barrier prevents using automation, customer trust in proper reassembly, warranty implications, and the physical safety requirements of working with power tools introduce organizational and liability friction to adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically governs this task, but practical barriers are high since it requires physical presence and hands-on tool use in varied environments. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of robotic systems capable of performing general disassembly/assembly work with hand and power tools far exceeds the loaded wage of a skilled technician, making AI automation economically infeasible at scale. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so the AI cost would effectively include building custom robotics, making it far more expensive than a human technician today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can independently disassemble and reassemble complex machines using hand and power tools. This task remains firmly in the domain of specialized robotics research, not production-ready systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general-purpose machine disassembly/assembly with hand and power tools in production; robotic manipulation for this level of task variety remains research-stage. |
Inspect machines and meters to determine causes of malfunctions and fix minor problems such as jammed bills or stuck products.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Inspect machines and meters to determine causes of malfunctions and fix minor problems such as jammed bills or stuck products.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a low-digitization, physically distributed sector with small firms and geographically dispersed assets. Adoption of automation in field service repair is historically slow, and this niche occupation shows no evidence of rapid AI deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Field service and repair for vending/amusement equipment is a low-digitization, physically-oriented trade sector with minimal AI/robotics adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Remote diagnostic cameras or AI-assisted troubleshooting checklists could modestly assist a technician in identifying a problem before or during a site visit, but the core repair work is manual and the augmentation uplift is limited compared to experienced field technicians' existing practices. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic guidance (e.g., troubleshooting manuals, error code lookup, predictive maintenance alerts) but offers little help with the actual physical clearing of jams or stuck products. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical inspection of machines, hands-on troubleshooting of mechanical jams, and manual repair work. Current AI systems cannot physically access, diagnose, or repair hardware; they lack embodied capability and dexterity to handle jammed bills or stuck products. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of machines—opening panels, clearing jams, physically inspecting mechanical parts—which current AI systems cannot perform without robotic embodiment far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | These machines are owned by third parties, and repairs often involve physical access permissions, liability for damage, and contractual service agreements. Regulatory requirements around cash handling and vending machine safety create additional friction against autonomous repair. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, but the task inherently requires physical presence and dexterity, which is a structural (not regulatory) barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An AI-assisted diagnostic system (if deployed) would still require a human technician to physically visit the site and perform repairs, so the full human labor cost is largely unavoidable. AI overhead would add cost rather than displace it. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical repair, so AI cost is essentially infinite relative to a human technician who can actually fix the jam. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can independently perform physical machine inspection and repair. Remote vision systems might assist diagnosis, but end-to-end malfunction detection and hands-on fixes remain outside the scope of current deployed automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical inspection and mechanical repair of vending/amusement machines; this remains squarely a hands-on field service task. |
Transport machines to installation sites.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Transport machines to installation sites.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task operates in laggard sectors with low automation adoption: small service businesses, physical logistics, and on-site installation work with minimal digitization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This is a physical, low-digitization task in a small-scale service trade with essentially no AI/autonomous vehicle adoption in this niche. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route planning or scheduling optimization, but these are small components of the transportation task itself. The primary work—physically moving equipment—remains unaided by current AI systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with route optimization or scheduling logistics, but it offers minimal assistance to the core physical task of loading and transporting machines. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Transporting physical machines to installation sites requires physical manipulation, navigation of real-world environments, and handling of large equipment. Current AI has no practical capability to perform this task autonomously end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical transport of heavy machines requires human driving, loading, and logistics that current AI systems cannot perform end-to-end; no software-based automation applies here. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical transportation requires human presence, valid driver licensing, insurance liability, and regulatory compliance for vehicle operation and hazmat (where applicable). These are hard barriers preventing full substitution without licensed personnel. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing beyond standard driving requirements, but physical handling, loading equipment, and liability for damage during transport create some operational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires human drivers, vehicles, fuel, and maintenance. AI systems capable of autonomous heavy equipment transport do not exist at commercial scale, making human labor the only viable option and thus cheaper than speculative automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no role in physical transport, so the human driver/mover remains the only viable and cost-effective option; autonomous trucking is not deployed for this niche use case. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical transportation of machines to installation sites. This task remains entirely dependent on human labor and basic vehicles/equipment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI products that autonomously transport vending or amusement machines; this remains a manual logistics/driving task. |
Make service calls to maintain and repair machines.
9CI 5–13 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Make service calls to maintain and repair machines.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | This is a laggard sector for automation—small service shops, often family-owned, with fragmented client bases and low capital investment in digital infrastructure. Adoption of AI-assisted tools is minimal; most work remains human-centric. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Field service and repair for vending/amusement machines is a low-digitization, physically-oriented trade with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist with remote diagnostic guidance or parts inventory management, but the core physical repair task resists augmentation; a technician still does the work in roughly the same time. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic guidance, scheduling, or parts lookup via mobile tools, but it does not meaningfully change the physical repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence on-site, hands-on mechanical/electrical repair work, and real-time diagnostic judgment that current AI cannot perform. While AI could assist with scheduling or remote diagnostics, the core task of physically servicing and repairing machines remains entirely dependent on human technicians. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physically traveling to a machine location, diagnosing mechanical/electronic faults, and performing hands-on repairs—no current AI system can perform physical service calls. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong adoption barriers exist: machine servicing often requires licensing (e.g., for vending machine operators in some jurisdictions), customer safety and liability concerns are high for equipment repair, and many clients require a certified human signature/warranty on repair work. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically exists, but physical access, tool manipulation, and on-site diagnosis create strong practical barriers to any remote or software-based substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task end-to-end, so the cost comparison is moot; a technician must be deployed regardless. Integration of AI diagnostics tools adds cost rather than replacing the human labor expense. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical labor involved, so there is no viable AI cost comparison—human technicians remain the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can independently conduct service calls, diagnose faults, and perform repairs on physical machines. The task demands embodied presence and mechanical skill that today's AI systems (including robots) cannot reliably execute at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical travel and hands-on repair of vending/amusement machines; this remains entirely a human field-service task. |
Adjust machine pressure gauges and thermostats.
7CI 0–15 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Adjust machine pressure gauges and thermostats.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Vending and amusement machine servicing is a physically distributed, low-digitization sector with small operators; adoption of AI or robotics for field service work remains minimal and is not a sector pattern of rapid technology adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Field repair and maintenance of vending/amusement machines is a low-digitization, physically-based trade with minimal AI adoption for hands-on tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While remote diagnostics and AI-assisted troubleshooting could help a technician identify what adjustment is needed, the actual physical act of adjusting gauges and thermostats offers limited room for AI assistance once the human is on-site. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic guidance, manuals, or troubleshooting suggestions via a mobile device, but offers no direct assistance with the physical adjustment itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Adjusting pressure gauges and thermostats requires physical manipulation of hardware components in real machines, which current AI systems cannot perform autonomously. This task fundamentally involves hands-on calibration that demands embodied action in the physical world. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of hardware components (gauges, thermostats) on-site, which current AI systems cannot perform without robotic embodiment that doesn't exist for this use case. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task involves physical work on machinery that must be performed on-site by a licensed technician who physically accesses and adjusts equipment. Safety regulations, liability, and the requirement for hands-on presence create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically governs this specific task, but the physical nature and on-site troubleshooting create practical barriers to any automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of any robotic system capable of performing such fine physical adjustments would far exceed the loaded wage of a skilled technician who performs this task routinely and flexibly across diverse machine types. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no viable path to perform this physical task, so any cost comparison favors the human technician who can actually complete the work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically adjust gauges and thermostats without a human operator. This is a mechanical task that requires robotic systems or human technicians, not software or vision-based AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical adjustment of vending/amusement machine pressure gauges and thermostats; this remains a manual technician task requiring hands-on manipulation and diagnostic judgment. |
Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers.
5CI 0–10 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail
Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | ATM servicing remains a hands-on, field-based occupation with minimal digitization of the repair work itself. Adoption of autonomous physical repair systems in this domain is negligible, and sector-wide adoption patterns show reliance on human technicians. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Field service and repair of physical vending/amusement/ATM equipment is a low-digitization, physical-labor sector with minimal AI/robotic adoption for hands-on repair tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostics (e.g., predictive alerts on wear patterns from transaction data) or part inventory management, but the core physical replacement task offers limited opportunity for meaningful human-AI collaboration without full automation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with diagnostics, parts identification, repair manuals, and troubleshooting guidance via mobile apps, improving technician efficiency even though the physical replacement itself remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of internal machine components, precise identification of worn parts by serial number or model, and hands-on replacement in a confined space. Current AI systems lack physical embodiment and dexterity to perform such work end-to-end, even with significant time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of hardware components inside machines in varied field locations, which current AI systems and robots cannot perform end-to-end.PB Physical dexterity and manual replacement of parts is far beyond current robotics/AI capability for general deployment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Financial institutions and ATM operators have strict liability requirements, regulatory oversight of security-critical components (card readers), and mandatory human certification/licensing for ATM maintenance. Legal and contractual obligations mandate human sign-off on repairs to payment systems. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars this repair work, but financial institutions often require vetted, insured, background-checked technicians for ATM access due to security and liability concerns. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of physical part replacement (if they existed) would require expensive mobile manipulation hardware, field deployment, and maintenance. This would far exceed the loaded wage of a human technician making service calls. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven substitute for physical repair, so the comparison defaults to the human being the only viable and thus cheaper (only) option in an all-in cost sense. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs physical part replacement in ATMs or vending machines autonomously. This requires robotics integrated with field service logistics, which does not exist in production for this specific domain at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously diagnoses and physically replaces ATM card reader components; this remains purely a human technician task. |
Install machines, making the necessary water and electrical connections in compliance with codes.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.4/5 · click for rater detail
Install machines, making the necessary water and electrical connections in compliance with codes.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs in low-digitization, physically distributed sectors (retail, hospitality, laundromats) with small operators. Adoption of automation in field service remains minimal and AI adoption is not displacing this work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation involves hands-on field service work in a low-digitization trade sector with minimal AI/robotics adoption for physical installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with code lookup, compliance checklists, or documentation, but the core task is physical installation requiring hands-on expertise; augmentation potential is minimal. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with reference lookup of code requirements or troubleshooting guidance, but offers little direct assistance to the physical connection work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical installation, handling of water/electrical infrastructure, and real-time code compliance validation on-site. Current AI systems cannot perform the embodied, hands-on work of connecting machines or the contextual judgment needed to ensure local code compliance. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical installation task requiring manual plumbing and electrical hookups on-site, which current AI systems cannot perform as they lack physical embodiment for such manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Electrical and plumbing work is heavily regulated; local building codes, licensing requirements, and liability concerns mean that a licensed human typically must perform or directly supervise these installations legally. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Electrical and plumbing connections are typically subject to local code compliance and often require licensed electricians/plumbers or certified technicians, creating real regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying specialized robotics, remote systems, and human oversight for this safety-critical physical task far exceeds the loaded wage of a trained technician performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical labor, so the human technician remains the only cost-effective option; AI cost is essentially infinite/inapplicable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical machine installation and water/electrical connections. This requires robotics in unstructured physical environments with safety-critical outcomes, which remains at research stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product installs vending/amusement machines with water and electrical connections; this remains firmly a human trade task. |
Related occupations — Installation, Maintenance & Repair
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.