Weighers, Measurers, Checkers, and Samplers, Recordkeeping
43-5111.00Median wage $46,380/yr53,300 employed (US)Rank #74 of 923 scored · top 8% by substitution
Weigh, measure, and check materials, supplies, and equipment for the purpose of keeping relevant records. Duties are primarily clerical by nature. Includes workers who collect and keep record of samples of products or materials.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution52
Exposure48
Augmentation54
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
28%
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%50
panel mean rating 3.0/5 → substitution pressure 50/100
Technical feasibility todayw 20%44
panel mean rating 2.8/5 → substitution pressure 44/100
Cost vs. human wagew 15%46
panel mean rating 2.8/5 → substitution pressure 46/100
Adoption barriersw 20%inverted — strong barriers lower the score70
panel mean rating 2.2/5 (barrier strength) → substitution pressure 70/100
Sector adoption velocityw 10%44
panel mean rating 2.8/5 → substitution pressure 44/100
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.
Count or estimate quantities of materials, parts, or products received or shipped.
84CI 72–95 · exposure 87 · augmentation 75 · importance 4.0/5 · click for rater detail
Count or estimate quantities of materials, parts, or products received or shipped.
84| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Warehousing, logistics, and manufacturing sectors—where this task concentrates—have rapidly adopted automated counting and measurement systems over the past decade. Major e-commerce and 3PL operators use these technologies in production at scale. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Warehousing and logistics have moderate digitization with growing automation adoption, but many smaller operations still rely on manual counting and paper-based processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can assist humans by providing real-time counts, flagging discrepancies, and automating routine tallies, allowing checkers to focus on exceptions and quality issues. The human remains in the loop for exception handling and sign-off, with significantly raised productivity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Barcode/RFID scanning and inventory software significantly speed up and reduce errors in human-performed counting tasks, keeping the worker in the loop for verification and exceptions. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Counting and estimating quantities of materials, parts, or products is a straightforward numerical task that can be fully automated using computer vision systems, barcode/RFID scanners, or weight-based measurement devices. Current systems can perform this end-to-end with >50% time savings and equal or superior accuracy compared to manual counting. |
| Task automatability | claude-sonnet-5 | 4/5 | Counting/estimating quantities can be largely automated using barcode scanners, RFID, computer vision, and weight-based sensors integrated with inventory systems, meeting the 50% time-saving bar in most standardized settings.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist; many organizations require human verification for compliance or liability reasons, but that verification can come after automated counting. Some organizational inertia remains, but no licensing or hard authorization requirements prevent automation itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement and minimal liability concerns for miscounts in most industries, though some regulated goods (pharma, hazardous materials) require certified sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated counting and measurement systems cost pennies to dollars per transaction in high-volume operations, while a human checker costs $20–30+ per hour. The AI cost is at least an order of magnitude cheaper when amortized over typical throughput. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated counting hardware/software has high upfront cost but very low marginal cost per count compared to a human recordkeeper's wage over repeated volume. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature production systems already perform this task reliably across warehousing, logistics, and manufacturing at scale. Barcode scanners, automated weighing systems, and vision-based counting are deployed daily in major distribution networks and fulfillment centers. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Warehouse automation products (scanners, vision-based counting systems, automated scales) are deployed at scale in logistics and manufacturing today, though edge cases with irregular items still require human checks. |
Collect or prepare measurement, weight, or identification labels and attach them to products.
76CI 55–97 · exposure 75 · augmentation 38 · importance 4.3/5 · click for rater detail
Collect or prepare measurement, weight, or identification labels and attach them to products.
76| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, food processing, and logistics—sectors where this task is common—have rapidly adopted automated weighing and labeling; production-scale deployments are standard in mid-to-large facilities. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing and warehousing sectors have moderate automation adoption, with labeling systems common in large-scale operations but slower penetration in smaller facilities and diverse product lines. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Augmentation potential is limited because the task is straightforward mechanical work; AI/automation does not meaningfully enhance human productivity if the human remains in the loop—it either automates or doesn't. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Barcode/label software, weight-capture systems, and print-on-demand labeling assist workers in generating accurate labels faster, though manual application and verification often remain human-performed. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | This task is highly automatable: measurement and weight acquisition can be done by sensors/scales, label generation by software, and label attachment by robotic systems. Current conveyor-integrated systems achieve ≥50% time savings at equal quality in production environments. |
| Task automatability | claude-sonnet-5 | 3/5 | Label generation and printing based on measurement data can be automated with scales, sensors, and label printers, but physical attachment to varied products often still requires manual dexterity or dedicated machinery integration.the mixed physical/informational nature limits full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent automation here; the main friction is setup and integration costs, customer acceptance of machine-applied labels, and occasional quality oversight requirements for critical products. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or human-contact requirements meaningfully block automation of labeling; it's a standard industrial process already often automated where volume justifies it. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Once deployed, automated weighing and labeling systems (scales, printers, applicators) cost orders of magnitude less per unit processed than paying a human operator, especially at production volumes. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated label-and-apply systems have high upfront capital cost but low marginal cost per unit at scale; for lower-volume or variable product lines, human labor remains cost-competitive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Deployed systems for automated labeling, weighing, and identification exist and operate reliably in manufacturing and logistics at scale—from industrial scales with barcode printers to robotic label applicators integrated into assembly lines. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated labeling systems (checkweighers, print-and-apply labelers) are deployed in many manufacturing and logistics settings, but they are narrow-purpose, product-specific installations rather than general-purpose AI, and many facilities still rely on manual labeling. |
Document quantity, quality, type, weight, test result data, and value of materials or products to maintain shipping, receiving, and production records and files.
75CI 75–75 · exposure 75 · augmentation 75 · importance 4.4/5 · click for rater detail
Document quantity, quality, type, weight, test result data, and value of materials or products to maintain shipping, receiving, and production records and files.
75| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | High-digitization sectors (logistics, e-commerce, manufacturing) are rapidly deploying vision-based and automated data-capture systems; major retailers and 3PLs now run production systems that feed automated documentation. Adoption is accelerating in supply chain operations. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Manufacturing, logistics, and warehousing have been rapidly adopting automated tracking, barcoding, and ERP-integrated recordkeeping systems, driven by supply chain digitization trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists human checkers by instantly flagging anomalies (weight mismatches, damage, label errors), prioritizing exceptions, and pre-filling records, allowing humans to focus on problem-solving and verification rather than routine data entry. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI and automated systems substantially reduce manual transcription burden and error rates, letting workers focus on verification and exception handling rather than raw data entry. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can reliably extract, classify, and log structured data from barcodes, labels, images, and test reports with high accuracy. Integration with warehouse management systems and databases enables end-to-end automation of documentation with minimal human intervention, easily meeting the 50% time-saving threshold for routine transactions. |
| Task automatability | claude-sonnet-5 | 4/5 | This is largely structured data entry and recordkeeping from sensor/scale/test outputs, which is well-suited to automation via IoT integration, barcode/RFID scanning, and software that auto-populates records, though some manual data capture and exception handling remains. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some sectors (pharmaceuticals, regulated food) have compliance documentation requirements, the task itself—recording data—has no legal mandate that a human must perform it. Organizational friction and desire for human spot-checks on anomalies provide modest friction, but no hard legal or licensing barrier prevents substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some industries (e.g., food, pharma, regulated materials) require documented chain-of-custody and quality certification, but the recordkeeping act itself is not typically restricted to licensed personnel. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Per-unit inference cost for image processing and data entry via automation is one to two orders of magnitude cheaper than human labor for high-volume repetitive documentation, especially when integrated with existing inventory systems that amortize platform costs. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once integrated, automated data capture and logging systems cost far less per transaction than manual recordkeeping, though initial sensor and software integration requires upfront investment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature computer vision and OCR products (e.g., from major cloud providers) are deployed at scale in logistics, manufacturing, and retail operations for barcode reading, weight capture, and defect detection. Error rates on standard materials and clean images are low; performance degrades only on damaged labels or unusual items. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Warehouse management systems, ERP platforms, and automated scales/scanners already capture and log quantity, weight, and quality data automatically in many production and logistics environments today. |
Operate scalehouse computers to obtain weight information about incoming shipments such as those from waste haulers.
74CI 72–76 · exposure 75 · augmentation 75 · importance 3.8/5 · click for rater detail
Operate scalehouse computers to obtain weight information about incoming shipments such as those from waste haulers.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Waste management and logistics are moderately digitized; some large haulers and facilities have adopted automated weight capture, but many smaller operations still rely on manual entry, indicating uneven but growing adoption. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Waste management and logistics are moderately digitized industries; automated weighbridge software is common but many smaller facilities still use manual or semi-manual processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists significantly by auto-populating weight fields, highlighting data anomalies, and flagging incomplete shipment records, allowing humans to focus on exception handling and quality assurance rather than routine data transcription. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where humans remain involved, automated scalehouse software greatly speeds up data capture, ticketing, and record reconciliation compared to manual weight logging. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Modern AI systems can reliably read digital weight displays, extract shipment metadata, and log data into databases with >50% time savings compared to manual entry and cross-checking. However, handling physical scale operation edge cases and exceptional circumstances requires residual human oversight. |
| Task automatability | claude-sonnet-5 | 4/5 | Reading scalehouse computer weight data and logging it is a structured, repetitive digital task easily handled by automated weighbridge software and sensor integration with minimal human input. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement exists for scalehouse data entry itself; however, some organizations may require human sign-off for legal/regulatory compliance on hazardous waste or controlled shipments, and legacy systems may resist integration. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Weight measurement for billing/regulatory purposes may need periodic calibration and audit trails, but no license is required to operate the software itself, so barriers are modest. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Computer vision inference, document processing, and database logging cost cents per shipment versus a loaded wage of $20–30/hour for a human weigher; AI cost per transaction is at least 10–50x cheaper at scale. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated scale systems require significant upfront capital but very low marginal cost per transaction compared to a dedicated human operator monitoring each weigh-in. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed OCR and vision systems routinely extract weight values from digital and analog scales in production environments; integration with warehouse management and ERP systems is mature. Error rates on clear digital displays are <1%, though poor image quality or unusual scale types reduce reliability. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Automated weighbridge/scalehouse systems with integrated software, RFID/license plate recognition, and automatic data capture are already deployed at scale in waste management and logistics facilities. |
Compare product labels, tags, or tickets, shipping manifests, purchase orders, and bills of lading to verify accuracy of shipment contents, quality specifications, or weights.
73CI 67–79 · exposure 70 · augmentation 75 · importance 4.5/5 · click for rater detail
Compare product labels, tags, or tickets, shipping manifests, purchase orders, and bills of lading to verify accuracy of shipment contents, quality specifications, or weights.
73| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Logistics, warehousing, and e-commerce sectors are rapidly deploying automated receiving and goods-in-check systems. Major shippers and 3PLs use AI-driven manifest verification at scale; adoption is moving from pilot to standard practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Warehousing and logistics have moderate digitization with growing use of scanning and inventory management software, but many smaller operations still rely on manual checking, placing this in the middle range of adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can flag mismatches and highlight discrepancies for human review, dramatically speeding the checker's work and reducing missed errors. The human retains judgment on ambiguous cases or exceptions, making this a strong augmentation scenario. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered document scanning, OCR, and automated cross-referencing tools significantly speed up discrepancy detection, letting workers focus on exceptions and physical verification rather than manual comparison. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current OCR and document comparison AI can reliably extract text from labels, manifests, and bills of lading, then compare fields for discrepancies with high accuracy. This covers the core verification workflow, though edge cases (damaged labels, ambiguous handwriting) and complex quality specifications may require human oversight, keeping it below 5. |
| Task automatability | claude-sonnet-5 | 4/5 | Comparing structured documents like labels, manifests, and purchase orders for discrepancies is a data-matching task well-suited to OCR plus AI reconciliation systems, though physical verification of actual weight/contents still requires human or sensor involvement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal barriers exist; no license is required for this task. Organizational friction (transition from manual processes, employee retraining) and customer preference for human verification in high-value shipments present minor friction but do not prevent automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this clerical verification role, though some industries with regulatory chain-of-custody or quality control requirements may prefer human sign-off for compliance and liability reasons. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated document scanning and comparison via cloud OCR APIs costs pennies per shipment, while a human weigher-checker earns $18–25/hour loaded cost. The cost differential is well over an order of magnitude. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated document scanning and matching software is cheap to run per transaction compared to a human recordkeeper's wage, though integration with physical inspection systems adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed document AI products (enterprise OCR platforms, logistics automation software) reliably perform label-to-manifest matching in production warehouse environments. Error rates on standard formats are low, though performance degrades on poorly scanned or non-standard documents. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Document reconciliation and OCR-based matching tools are deployed in logistics and warehousing today, but full end-to-end verification including physical inspection still relies on human checkers, so current products cover only part of the task reliably. |
Store samples of finished products in labeled cartons and record their location.
69CI 61–77 · exposure 70 · augmentation 50 · importance 4.3/5 · click for rater detail
Store samples of finished products in labeled cartons and record their location.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is uneven: pharma, biotech, and large-scale food/chemical manufacturing increasingly deploy automated sample storage; small labs and contract testing facilities remain manual. Overall adoption is moderate, with pockets of advanced deployment rather than industry-wide velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This task sits within manufacturing/quality-control and warehousing, sectors with moderate but not fast AI/automation adoption, especially at smaller facilities where this role is common. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance to a human performing this task. Sample storage and location recording are straightforward procedural work with little opportunity for AI-powered advice or optimization that improves human performance—the task is better displaced than augmented. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Barcode scanners, inventory management software, and automated labeling tools significantly speed up and reduce errors in recording sample locations while a human still handles physical placement. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | This task is highly automatable: robotic systems can store samples in labeled cartons using computer vision and automated conveyor/storage systems, while barcoding and database logging replace manual recording. The physical + record-keeping dual nature lends itself to full end-to-end automation with ~60–70% time savings. |
| Task automatability | claude-sonnet-5 | 4/5 | This is a highly routine physical-storage-plus-data-entry task; robotics and barcode/RFID systems combined with software can log location automatically, though the physical placement of samples still requires some manual handling. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automating sample storage and location recording; this task does not require human judgment or sign-off. The main friction is organizational inertia and capital investment requirements, but no hard legal barrier prevents substitution. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barriers prevent automating storage and recordkeeping of product samples. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Robotic sample storage systems and automated inventory logging are capital-intensive upfront but have low per-unit cost once deployed. For high-volume sampling operations, the per-task cost (equipment amortization + power + maintenance) is typically 2–5× cheaper than loaded human labor, especially when 24/7 operation is factored in. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Barcode/RFID tracking software is cheap relative to labor for the recordkeeping portion, but the physical carton handling still requires human labor or expensive robotics, keeping overall cost roughly comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Warehouse automation and sample-management systems with robotic storage-and-retrieval (ASRS) are deployed in pharmaceutical, food, and chemical industries today. Integration of barcode scanning and inventory databases is mature; the gap is that fully autonomous sample handling (liquid/solid/fragile handling) remains sector-specific rather than universal, placing it at high feasibility with some remaining edge-case limitations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Warehouse management systems and barcode scanning are mature and widely deployed for tracking inventory locations, but fully automated physical placement of sample cartons in labeled storage is less common in this specific role and often still hybrid with human handling. |
Weigh or measure materials, equipment, or products to maintain relevant records, using volume meters, scales, rules, or calipers.
67CI 55–79 · exposure 62 · augmentation 63 · importance 4.4/5 · click for rater detail
Weigh or measure materials, equipment, or products to maintain relevant records, using volume meters, scales, rules, or calipers.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, logistics, and supply-chain sectors have already deployed automated weighing and measurement at scale; adoption is well-established and accelerating across facilities management and quality control. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing, logistics, and warehousing sectors are adopting automated measurement and IoT scales at a moderate pace, with pilots and partial deployment more common than full-scale replacement in smaller operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted visual inspection and anomaly detection in measurement data can help workers flag outliers and verify unusual readings, providing moderate productivity gains alongside or before full automation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Digital scales, automated data capture, and integrated recordkeeping software significantly speed up and reduce errors in the measuring and logging process, letting workers focus on exceptions and quality checks. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Weighing and measuring can be largely automated with sensors, scales, and computer vision systems that feed directly into digital record-keeping; however, irregular or delicate items and edge cases may still require human judgment, preventing a full 5. |
| Task automatability | claude-sonnet-5 | 3/5 | The physical act of weighing/measuring requires sensors and possibly robotic handling, but many workflows already use automated scales/meters feeding directly into digital records, reducing but not eliminating the human role of positioning materials and verifying readings., physical placement of goods. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal barriers exist; automation is standard in regulated industries (food, pharma) and compliance actually favors automated, traceable measurement records over manual ones, reducing organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this task typically, though quality control and calibration standards (e.g., NIST, ISO) exist; adoption is limited mainly by capital cost and workflow integration rather than legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated sensors and IoT scales cost pennies per measurement with minimal ongoing labor, making the all-in cost orders of magnitude lower than paying a human worker for repetitive weighing and recording tasks. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated weighing/measuring hardware has upfront capital costs but low marginal cost per use; for high-volume standardized operations it's cheaper than labor, but for low-volume or variable tasks the equipment and integration costs make it comparable to human cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed automated weighing systems, dimensional scanning, and vision-based measurement are well-established in production environments across manufacturing and logistics; integration with recordkeeping is standard practice. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | IoT-enabled scales, RFID, and automated measurement systems are deployed in warehouses and manufacturing, but many settings still rely on manual measurement and logging, especially for varied or non-standard items. |
Sort products or materials into predetermined sequences or groupings for display, packing, shipping, or storage.
66CI 55–77 · exposure 62 · augmentation 50 · importance 3.7/5 · click for rater detail
Sort products or materials into predetermined sequences or groupings for display, packing, shipping, or storage.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Sorting automation is already deeply embedded in logistics, e-commerce fulfillment, and manufacturing. Major retailers and logistics providers (Amazon, DHL, etc.) have high-velocity adoption with measurable workforce displacement in these sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Warehousing and logistics sectors show moderate-to-strong adoption of automated sortation and robotics, though adoption is uneven across firm sizes and industries, with many still using manual processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted sorting can help humans by prioritizing items, flagging defects, or optimizing sequences, raising throughput modestly. However, the task's repetitive, structured nature means augmentation is less central than in judgment-heavy roles. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted scanning, labeling, and sorting-guidance tools help workers sort faster and more accurately, though the physical sorting action itself still requires human or robotic execution. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Sorting products into predetermined sequences using computer vision and robotic systems can be largely automated end-to-end with significant time savings. Modern sorting systems routinely achieve >50% time reduction compared to manual sorting, though some edge cases (unusual shapes, fragile items) may require human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | Sorting by predetermined rules can be automated with vision systems and robotics/conveyor sortation, but many implementations still require physical handling that pure AI software cannot do alone; the cognitive sorting logic is highly automatable while physical execution needs robotics integration. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for product sorting automation. Main friction comes from capital equipment requirements, transition costs, and organizational change management rather than legal mandates or liability asymmetries. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human sorting; the main barriers are capital investment and physical/logistical integration rather than regulatory or liability concerns. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | The cost of automated sorting (hardware, maintenance, integration) is typically 2–5× lower per item sorted than human labor over system lifetime, especially for high-volume operations. Some small-scale or low-throughput contexts may see weaker economics. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated sortation equipment has high upfront capital cost but low marginal cost; for high-volume operations it can be cheaper long-term, but for smaller-scale or variable tasks the equipment and integration cost may exceed human labor cost initially. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed sorting automation systems exist in production at warehouses, fulfillment centers, and manufacturing facilities (conveyor sorters, robotic bin picking, automated tape dispensers). Reliable performance is demonstrated at scale, though integration costs and domain-specific tuning are required for new applications. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated sortation systems (barcode/vision-guided conveyors, warehouse robotics) are deployed at scale in logistics, but many smaller operations still rely on manual sorting, and edge cases/damaged or irregular items reduce reliability. |
Inspect products and examination records to determine the number of defects per worker and the reasons for examiners' rejections.
52CI 50–55 · exposure 50 · augmentation 75 · importance 4.3/5 · click for rater detail
Inspect products and examination records to determine the number of defects per worker and the reasons for examiners' rejections.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing and QA operations show growing adoption of AI-driven quality analytics, but deployment is uneven; large manufacturers lead while mid-market and small shops lag. Pilots are common; full automation of this judgment task is still emerging. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing and quality control show growing adoption of automated inspection (computer vision QC systems), but widespread deployment across smaller manufacturers remains uneven compared to leading digitized industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants can significantly boost human productivity by auto-aggregating defect counts, surfacing patterns, and flagging anomalies for human investigators to review. This keeps the quality analyst in the loop while dramatically accelerating record analysis and reason identification. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered defect detection and analytics dashboards significantly help human quality inspectors identify patterns and root causes of rejections faster than manual record review alone. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate parts of this task—scanning examination records, flagging patterns in rejection codes, and generating defect counts per worker—but requires human judgment to investigate root causes and interpret context-dependent rejection reasons. Setup and oversight overhead moderate the time savings. |
| Task automatability | claude-sonnet-5 | 3/5 | Analyzing defect records and computing rejection statistics is well within automation reach, but physical inspection of products to verify defects requires sensing/vision integration that adds setup complexity beyond off-the-shelf software alone. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality and compliance oversight typically require human sign-off on conclusions about worker performance and root causes, creating organizational friction. Regulatory regimes in manufacturing often require documented human review of defect analysis. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this quality-control task, though some manufacturing contexts require human sign-off for defect classification affecting safety-critical products, creating mild friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inference for record scanning and aggregation is inexpensive, but integration into quality systems and human oversight of flagged cases brings total cost closer to a semi-skilled recordkeeper's loaded wage. Not a clear cost win. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Data analysis portions are cheap to automate, but machine vision inspection systems require capital investment in cameras/sensors and calibration, making all-in cost roughly comparable rather than dramatically cheaper for many production lines. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for quality data analysis and anomaly detection in manufacturing, but error rates remain material when interpreting nuanced rejection reasons and worker-level attribution. Deployment is common in larger operations but not uniformly reliable across contexts. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated visual inspection and QC analytics systems exist and are deployed in manufacturing, but the combined task of physically inspecting products plus cross-referencing examiner rejection reasons is typically only partially automated in production. |
Examine products or materials, parts, subassemblies, and packaging for damage, defects, or shortages, using specification sheets, gauges, and standards charts.
52CI 46–57 · exposure 50 · augmentation 75 · importance 4.3/5 · click for rater detail
Examine products or materials, parts, subassemblies, and packaging for damage, defects, or shortages, using specification sheets, gauges, and standards charts.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and logistics sectors are actively deploying AI vision systems for quality control, with many pilot and early production implementations visible in automotive, electronics, and food industries. Adoption is faster in larger firms and high-volume operations where ROI justifies integration costs. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing and warehousing have moderate automation adoption with vision-based QC systems increasingly common, but overall sector digitization and deployment depth remains uneven compared to leading digital-native industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Computer vision systems provide powerful assistance by highlighting suspect areas, automating tedious visual scans, and cross-referencing specifications, allowing human inspectors to focus on judgment calls and complex cases. This augmentation substantially raises productivity when paired with human oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted defect detection tools significantly speed up and improve consistency of human inspectors by flagging anomalies, even when full automation isn't achieved, making this a strong augmentation use case. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Visual inspection for damage and defects can be partially automated using computer vision and image recognition systems, but integrating specification matching, gauge reading, and standards comparison still requires significant setup and human oversight. Current AI handles some defect detection reliably but struggles with the full end-to-end workflow including documentation. |
| Task automatability | claude-sonnet-5 | 3/5 | Machine vision and sensor-based inspection systems can automate much of visual defect detection against specs, but many implementations still require physical handling and human confirmation for edge cases, so it's not a full end-to-end replacement across all contexts. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality assurance and defect identification often have contractual, liability, and regulatory implications (especially in safety-critical industries like automotive or pharmaceuticals), creating organizational friction. Some jurisdictions and industries require documented human sign-off on quality checks, though barriers vary widely by sector. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some industries (food safety, pharma, aerospace) impose quality/regulatory documentation requirements that favor human sign-off, but for general inspection tasks there's limited licensing or legal restriction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While vision systems have low per-unit inference cost, the integration overhead, custom training, camera setup, and required human oversight for verification keep total deployed cost comparable to or higher than hourly inspector wages in many contexts. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Machine vision systems can be cheaper per-unit at high volume once installed, but capital costs for cameras, sensors, and integration plus maintenance can make costs comparable to human labor especially at lower throughput. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Computer vision systems are deployed in manufacturing QA settings to detect visual defects, but they typically operate within narrow, controlled scopes (specific product types, lighting conditions) and require human verification. Production deployments exist but often as aids rather than autonomous end-to-end checkers. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated visual inspection and gauging systems are deployed in manufacturing and logistics today, but reliability varies significantly by product type, material variability, and defect complexity, often requiring human backup checks. |
Fill orders for products and samples, following order tickets, and forward or mail items.
45CI 39–51 · exposure 45 · augmentation 50 · importance 3.9/5 · click for rater detail
Fill orders for products and samples, following order tickets, and forward or mail items.
45| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large logistics and e-commerce firms have invested heavily in robotic warehousing and automation, but many smaller manufacturers and distributors still rely on manual order fulfillment; adoption is growing but unevenly distributed across sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and logistics adopt automation steadily but unevenly; most fulfillment still relies heavily on manual labor except in large e-commerce operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted order management (automated picks suggestions, label generation, inventory tracking dashboards) meaningfully speeds execution and reduces errors when operators remain in the loop, without requiring full end-to-end automation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven inventory systems, barcode scanning, and route optimization assist workers in verifying and completing orders faster, though the physical act remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | The task involves clear procedural steps (reading order tickets, picking items, packaging, forwarding) that are partially automatable with warehouse automation systems, but end-to-end automation with 50% time savings at equal quality requires handling variable products, addresses, and final quality checks—achievable in controlled environments but not consistently across diverse real-world order types. |
| Task automatability | claude-sonnet-5 | 3/5 | Order-fulfillment based on tickets involves reading structured data and picking/packing physical items; the data-matching part is automatable but physical retrieval and packaging still requires human or robotic manipulation not universally deployed.5 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Physical constraints (handling fragile or oversized items, variable packaging requirements) and customer expectation for human quality assurance create moderate friction, though no strict legal or licensing barriers prevent automation; organizational adoption of full automation is common in high-volume operations but slower in smaller facilities. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirements, but physical handling of diverse items, packaging variability, and liability for shipping errors create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Warehouse automation (robots, conveyors, vision systems) requires substantial capital and integration costs that approach or exceed the loaded wage of a single warehouse worker performing picking and packing, especially for smaller or variable order volumes. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic and automated fulfillment systems require significant capital investment in conveyors, robotics, and integration, which is costly relative to lower-wage warehouse labor except at very large scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While individual components (inventory lookup, label printing, basic sorting) have deployed solutions, no current system reliably performs the full task end-to-end including item selection, packaging accuracy, and shipment logistics verification without human oversight and error rates remain material in production environments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Warehouse automation (pick-to-light, robotic picking, automated sortation) exists in large fulfillment centers but is far from universal for this occupation broadly, and mailing/forwarding still often involves manual steps. |
Remove from stock products or loads not meeting quality standards, and notify supervisors or appropriate departments of discrepancies or shortages.
33CI 30–35 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Remove from stock products or loads not meeting quality standards, and notify supervisors or appropriate departments of discrepancies or shortages.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is concentrated in large-scale manufacturing with standardized products (automotive, electronics); smaller operations and mixed-product warehouses lag significantly. Overall sector adoption remains pilot-heavy rather than normalized production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and quality control in physical goods sectors have relatively low digitization and automation adoption compared to information-based industries, with automation limited mostly to large-scale distribution centers. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered defect detection can highlight suspicious items for faster human inspection, reducing fatigue and speeding triage decisions. The human remains the final judge, but AI assists in focusing attention and recording discrepancies systematically. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered vision systems and defect-detection software can meaningfully assist checkers by flagging likely discrepancies, speeding up the identification phase even though physical removal and final judgment remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Quality assessment requires visual inspection and judgment that varies by product type and standards, though removal from stock is straightforward. Current AI can identify some defects via computer vision but struggles with the full range of subjective quality criteria and context-dependent decisions that humans handle reliably. |
| Task automatability | claude-sonnet-5 | 2/5 | The physical act of removing nonconforming products from stock requires manipulation and judgment about materials that current AI cannot perform end-to-end; only the detection/notification component is automatable, not the full task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Liability for missed defects and potential product recalls create organizational friction and supervisory oversight requirements, though no strict licensing barrier exists. Companies face reputational and legal risk if automation causes quality failures. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There are few licensing requirements for this role, though liability for shipping defective goods and organizational trust in physical handling create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Computer vision infrastructure and integration costs, plus required human oversight for marginal cases, approach or exceed the cost of a single human checker in many contexts. Only in high-volume, standardized manufacturing does AI achieve cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying computer vision plus robotic sorting/removal systems requires significant capital investment in sensors, integration, and maintenance, often exceeding the cost of a warehouse worker performing this task for lower-volume operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed computer vision systems exist for specific defect detection (e.g., manufacturing), but general-purpose quality assessment across diverse product types remains inconsistent and requires human oversight. Most production systems still rely on human inspectors with AI as a narrow-scope assistant rather than autonomous performer. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Machine vision quality inspection systems exist in some manufacturing settings, but the combined task of physically pulling flagged items and routing notifications reliably across diverse warehouse/stock environments is not a mature deployed product. |
Communicate with customers and vendors to exchange information regarding products, materials, and services.
33CI 30–35 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Communicate with customers and vendors to exchange information regarding products, materials, and services.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Small to mid-sized weighing/measuring operations and many logistics firms remain low-digitization environments where customer communication still relies on phone and email handled by humans. Adoption of AI communication tools in this sector is slower than in high-tech or finance. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This occupation is in warehousing/logistics/quality control, a sector with generally slower AI adoption compared to information or finance industries, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by drafting routine responses, logging interactions, and summarizing vendor communications, meaningfully improving a human operator's productivity without replacing the human judgment needed for complex product or service discussions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting communications, summarizing vendor/product information, and translating or organizing data, improving efficiency while the human retains final judgment and relationship management. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft routine communications and respond to templated inquiries, the task requires nuanced exchange of information about product specifications, problem-solving, and relationship management that depends heavily on domain context and real-time negotiation. Current systems cannot reliably handle the full range of customer/vendor interactions with equal quality and time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | Routine transactional communication (order confirmations, standard status updates) could be partly automated, but much of this involves relationship management, negotiation, and ad hoc problem-solving that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict legal requirements for a human to conduct routine product inquiries, organizational policies, customer preference for human contact, and liability concerns about miscommunicated specifications create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer/vendor relationships often benefit from human trust and accountability, creating moderate organizational friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of customer communication systems with backend inventory and vendor systems adds non-trivial cost, and human oversight of vendor communications remains necessary to avoid costly errors. The all-in cost per meaningful interaction remains comparable to or exceeds a junior employee's time. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply draft or triage routine messages, but human oversight is still needed for accuracy on materials/specs and relationship continuity, keeping blended costs closer to human levels for full task coverage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and email automation exist for basic inquiries, but deployed systems struggle with complex product discussions, exception handling, and the judgment required to represent the organization in vendor negotiations. Production use remains largely limited to FAQ responses rather than substantive information exchange. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and email-drafting tools exist for customer/vendor communication, but reliable autonomous handling of varied, context-specific product/material/service exchanges in this specific role is not demonstrated in production at scale. |
Transport materials, products, or samples to processing, shipping, or storage areas, manually or using conveyors, pumps, or hand trucks.
26CI 26–26 · exposure 16 · augmentation 25 · importance 3.7/5 · click for rater detail
Transport materials, products, or samples to processing, shipping, or storage areas, manually or using conveyors, pumps, or hand trucks.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some logistics and manufacturing sectors are piloting automated material transport, adoption remains limited and concentrated in large facilities with high-volume, standardized workflows. Most small to mid-size operations still rely on manual transport due to setup costs and complexity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and logistics have growing but still limited robotic adoption; most material transport in this occupation remains manual or reliant on conventional (non-AI) conveyors and hand trucks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered routing or optimization tools could assist workers in planning transport routes or checking inventory, but the core physical movement itself offers minimal augmentation potential since it remains fundamentally a human-powered or dedicated robotic task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven route optimization or inventory tracking can indirectly support transport logistics, but it provides minimal direct assistance to the physical act of moving materials. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI systems cannot reliably operate physical transportation of materials without heavy robotics integration, which remains technically challenging and expensive for diverse environments. While conveyor systems can be automated, the manual handling component and need to navigate variable warehouse layouts with safety constraints severely limits full task automation today. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical material-handling task requiring manipulation of objects across a facility, which is not addressed by current AI software systems; only robotics could automate it, and general-purpose robotic solutions for varied transport tasks are not yet mature or widely deployed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Safety regulations and workplace compliance requirements create some friction, and physical task substitution requires specialized robotics infrastructure that poses integration barriers. However, no licensing requirement or human sign-off mandate exists for automation itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but physical workspace constraints, safety regulations around automated equipment, and capital costs create moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous material handling robots and integrated systems remain significantly more expensive than human labor when accounting for hardware, installation, maintenance, and integration costs, especially for variable or multi-step transport tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical automation (robots, conveyors, AGVs) requires significant capital investment, integration, and maintenance, often exceeding the cost of human labor for lower-volume or variable material handling tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No mature deployed product reliably performs end-to-end material transport autonomously in production at the scale required for general warehouse or manufacturing settings. Autonomous systems exist only in highly controlled, mapped environments (e.g., specialized warehouses) and are not broadly deployable. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mature AI/robotics product reliably performs generalized manual transport of materials via hand trucks or mixed conveyance methods across diverse warehouse/processing settings today; existing AMRs handle narrow, structured cases only. |
Signal or instruct other workers to weigh, move, or check products.
24CI 14–35 · exposure 20 · augmentation 38 · importance 4.0/5 · click for rater detail
Signal or instruct other workers to weigh, move, or check products.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is embedded in physical, floor-based operations in lagging-adoption sectors (manufacturing, warehousing) where human coordination remains standard practice, with minimal AI adoption pressure or existing pilots for autonomous worker instruction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehouse and manufacturing settings are moderate-to-slow adopters of AI compared to information/professional services, though some automation (barcode systems, WMS alerts) is already common infrastructure, not full AI-driven coordination. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by recommending which workers to signal or generating instruction text, but the core task of actually signaling and directing workers in real time requires human authority and presence, limiting augmentation value. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based inventory and quality control systems can flag items and generate work orders, helping a supervisor decide who to direct and when, improving efficiency without replacing the coordination role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires real-time communication and coordination with other workers on a physical production floor, which current AI cannot reliably execute. While AI could generate instructions or notes, actively signaling and instructing workers in a dynamic environment demands human presence and responsiveness that AI cannot achieve today. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires real-time physical coordination with other workers on a shop floor or warehouse, which current AI systems cannot perform end-to-end without human presence and physical interfacing., though some notification/dispatch logic could be automated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations and workplace protocols typically require human supervisors or coordinators to directly oversee and instruct workers on production floors, creating a legal and organizational requirement for human involvement in task direction and quality assurance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational friction exists since this involves interpersonal instruction and coordination with human workers who need to trust and follow directions, which may resist pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any attempt to automate this through robotics or AI agents with physical presence would be far more expensive than a human supervisor or coordinator earning typical weigher/checker wages. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Basic automated flagging/alert systems are cheap, but full replacement of the coordinating role including judgment calls and exception handling still requires human oversight, keeping costs comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously signal or instruct workers in real warehouse or factory settings. This requires embodied presence, real-time responsiveness to worker queries, and authority recognition that current AI agents cannot provide. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated alert systems and warehouse management software exist that flag items needing checks, but the actual signaling/instructing of human workers to physically act is typically mediated by simple software triggers, not autonomous AI agents managing worker coordination reliably. |
Collect product samples and prepare them for laboratory analysis or testing.
23CI 16–30 · exposure 20 · augmentation 38 · importance 3.8/5 · click for rater detail
Collect product samples and prepare them for laboratory analysis or testing.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains limited to large-scale pharma, food, and petrochemical operations with high-volume standardized processes. Most mid-market and small manufacturing, agriculture, and QA functions still rely on manual sampling due to cost and complexity barriers. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, quality control, and lab-adjacent physical sectors are slower AI adopters compared to office/professional services, with automation focused on data logging rather than physical sample handling. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist via real-time sensor data, automated sample logging, scheduling optimization, and flagging anomalous batches. These tools improve efficiency and reduce manual recording errors, but a human sampler must remain in the loop for physical collection, judgment, and protocol compliance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with tracking, labeling, chain-of-custody documentation, and scheduling, but offers little assistance with the physical act of collecting and preparing samples. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While sample collection can be partially automated (e.g., robotic sampling systems, sensor-based triggers), the task requires physical manipulation in often unstructured environments, selective judgment about sample representativeness, and compliance with domain-specific protocols. Current AI systems cannot reliably handle the full end-to-end task with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical sample collection and preparation requires manual handling of materials, which current AI systems cannot perform end-to-end without robotics; some data recording aspects could be automated but the core physical task cannot. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory requirements (FDA, ISO, GMP standards) often mandate that samples be collected, handled, and documented by trained personnel with traceability and chain-of-custody accountability. Liability and validation concerns around sampling integrity create strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically, but quality control, chain-of-custody, and safety protocols in sampling create procedural friction that slows substitution by non-human agents. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated sampling systems (robots, sensors, inline analyzers) carry high capital and integration costs. For most organizations, the all-in cost of automation setup and maintenance exceeds the loaded wage of a single samplers, making it economically unfavorable except in high-volume standardized settings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical sampling requires human presence, mobility, and dexterity; deploying robotics for this narrow task would be far more costly than employing a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic systems exist for narrowly defined sampling scenarios (e.g., pharma, food quality), but they require heavy setup and human oversight. No mature off-the-shelf product reliably performs this task independently across varied product types and conditions at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No widely deployed AI product autonomously collects and prepares physical product samples for lab testing; this remains a manual/robotic task with only nascent automation in specialized industrial settings. |
Unload or unpack incoming shipments.
21CI 15–26 · exposure 8 · augmentation 25 · importance 3.3/5 · click for rater detail
Unload or unpack incoming shipments.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption is minimal; most receiving operations rely on manual labor. While some large logistics firms experiment with robotic sorting and palletizing, general-purpose unpacking automation remains in early/pilot phases, not production deployment at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and logistics are adopting automation (e.g., AMRs, conveyor systems) but full unloading/unpacking automation remains a slow-moving physical-world challenge with limited penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Vision-based AI could assist workers by identifying contents or flagging anomalies in shipments, but the core physical task of unpacking remains primarily human-executed. Augmentation value is modest because the limiting factor is manual dexterity, not information processing. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can support scheduling, tracking, and identifying incoming shipment contents via barcode/vision scanning, but this offers only modest assistance in the underlying physical unloading task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Unpacking requires variable spatial reasoning, dexterity with irregular objects, and adaptation to different packaging formats. Current AI vision systems can detect and identify items, but end-to-end unpacking with handling of diverse materials and fragile items remains beyond practical automation without substantial custom engineering. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically unloading and unpacking shipments requires manual manipulation of boxes/pallets in variable environments, which current general AI systems cannot perform end-to-end; this requires robotics, not language/vision AI alone. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical task execution in uncontrolled receiving environments presents significant technical barriers, and no regulatory mandate requires human presence. However, variability in shipment composition and potential liability for damage to goods create practical friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but physical workspace variability, safety requirements, and capital costs create practical friction to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic unpacking systems are capital-intensive ($100k+) and require ongoing maintenance, integration, and adaptation, far exceeding the loaded labor cost of a warehouse worker performing this task at standard wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized unloading robots and their integration/maintenance costs exceed the cost of manual labor for most receiving operations, especially for irregular or mixed shipments. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably unpacks general incoming shipments in production settings. Robotic unpacking exists only in narrow, controlled contexts (e.g., specific box types, pre-arranged layouts), not as a general solution for typical warehouse or receiving operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | While warehouse robotics exist for narrow palletized unloading in controlled settings, no widely deployed product reliably unpacks arbitrary incoming shipments at scale today. |
Maintain, monitor, and clean work areas, such as recycling collection sites, drop boxes, counters and windows, and areas around scale houses.
19CI 15–24 · exposure 8 · augmentation 13 · importance 3.5/5 · click for rater detail
Maintain, monitor, and clean work areas, such as recycling collection sites, drop boxes, counters and windows, and areas around scale houses.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Recycling facilities and materials handling are typically small-to-medium operations with low digitization and limited capital investment in automation; adoption of autonomous cleaning systems remains negligible in these sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Physical facility maintenance in industrial/recycling settings is a low-digitization sector with minimal AI or robotics adoption for general cleaning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Monitoring systems (cameras, sensors) could assist workers in identifying cleaning needs or checking area conditions, but augmentation is limited since the core physical tasks cannot be substantially AI-assisted while maintaining human control. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no productivity assistance for manual cleaning and area maintenance work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical maintenance, monitoring, and cleaning require dexterous manipulation and environmental adaptation that current AI systems and robots cannot reliably perform across varied real-world conditions. While some monitoring could be aided by vision systems, the core cleaning and maintenance tasks remain beyond reliable automation today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical cleaning and maintenance task requiring manipulation of physical objects and spaces; no off-the-shelf AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements, liability concerns around autonomous systems damaging facilities or safety hazards, combined with practical setup challenges in unstructured environments, create moderate friction against adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers restrict who cleans an area, but the physical nature of the task itself is the real barrier to automation rather than regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotics solutions for cleaning and site maintenance are capital-intensive and require significant integration costs, making them substantially more expensive than hiring workers for these tasks, especially in lower-wage recycling and materials handling contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical cleaning, so AI cost is effectively infinite relative to human labor for this task; robotic cleaning solutions exist only in narrow, expensive, non-general contexts. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs the full task of cleaning and maintaining diverse physical work areas autonomously. Cleaning robots exist but are narrowly scoped to specific environments and require substantial setup and human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical cleaning of work areas, drop boxes, or scale houses; this remains a manual labor task. |
Related occupations — Office & Administrative Support
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.