Laborers and Freight, Stock, and Material Movers, Hand
53-7062.00Median wage $40,240/yr2,950,280 employed (US)Rank #508 of 923 scored · top 55% by substitution
Manually move freight, stock, luggage, or other materials, or perform other general labor. Includes all manual laborers not elsewhere classified.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution27
Exposure16
Augmentation34
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
13 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
8%
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%17
panel mean rating 1.7/5 → substitution pressure 17/100
Technical feasibility todayw 20%15
panel mean rating 1.6/5 → substitution pressure 15/100
Cost vs. human wagew 15%18
panel mean rating 1.7/5 → substitution pressure 18/100
Adoption barriersw 20%inverted — strong barriers lower the score66
panel mean rating 2.3/5 (barrier strength) → substitution pressure 66/100
Sector adoption velocityw 10%19
panel mean rating 1.8/5 → substitution pressure 19/100
Task breakdown (13 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Record numbers of units handled or moved, using daily production sheets or work tickets.
86CI 72–100 · exposure 87 · augmentation 75 · importance 4.0/5 · click for rater detail
Record numbers of units handled or moved, using daily production sheets or work tickets.
86| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Logistics, warehousing, and manufacturing—the primary sectors employing these workers—have been rapidly adopting automated inventory and production tracking systems for years, with widespread deployment across large and mid-sized operations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Warehousing and logistics have moderate digitization with many large operators adopting scanning and automated tracking, but a large tail of smaller operations still relies on manual paper-based tracking. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered tracking systems augment workers by automating routine logging, reducing manual paperwork burden, and providing real-time data visibility that helps workers optimize their own efficiency and task prioritization. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where full automation isn't in place, handheld scanners and mobile apps significantly speed up and reduce errors in recording units handled compared to manual tallying. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Recording unit counts from work tickets is a straightforward data entry task that can be fully automated via OCR, barcode scanning, or integration with warehouse management systems, easily achieving 50%+ time savings at equal accuracy compared to manual transcription. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording unit counts is a structured data-entry task that barcode scanners, RFID, and warehouse management systems can already capture and log automatically, largely replacing manual tallying and paper tickets. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | There are no licensing, regulatory, or legal requirements mandating human involvement in recording unit counts; the task is purely administrative data capture with no liability barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human recording; the main barrier is capital cost of scanning/WMS infrastructure and integration with existing workflows. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated systems (barcode scanners, RFID, software integration) have near-zero per-transaction cost after initial setup, orders of magnitude cheaper than paying a human to manually record each unit moved. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once scanning infrastructure is installed, the marginal cost of automatically logging units is far below paying a worker's time to manually record data on paper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Deployed systems for automated inventory tracking, barcode scanning, and production logging are mature and operate reliably in warehouses and logistics facilities at scale today. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Warehouse management systems with barcode/RFID scanning and automated production tracking are widely deployed in production logistics environments today, though many small operations still use paper tickets. |
Read work orders or receive oral instructions to determine work assignments or material or equipment needs.
43CI 35–50 · exposure 30 · augmentation 63 · importance 4.1/5 · click for rater detail
Read work orders or receive oral instructions to determine work assignments or material or equipment needs.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large logistics and warehousing firms are piloting voice-to-dispatch and AI-assisted work-order systems, but widespread production deployment remains limited; adoption is faster in digitized, high-volume operations but slower in small/medium facilities. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | placeholder |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Voice assistants and AI transcription already augment workers by reducing manual data entry and clarifying ambiguous instructions; AI can draft assignments or flag missing details, keeping the human supervisor in the loop while speeding up the interpretation cycle. |
| Augmentation potential | claude-sonnet-5 | 3/5 | placeholder |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can parse written work orders and transcribe oral instructions reliably, but interpreting context-dependent material/equipment needs and generating actionable assignments still requires human judgment. Only narrow, templated scenarios achieve 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Understanding a work order or oral instruction is text/speech comprehension AI can handle, but this task is embedded in a physical workflow where the 'reading' is a small trigger for subsequent manual action, limiting standalone time savings.','rating_note':none}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates a human perform this task, and warehouses already use digital systems; adoption is blocked mainly by organizational inertia and the need for reliable integration with legacy warehouse management systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | placeholder |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Speech recognition, text parsing, and instruction-routing infrastructure are commodity-cheap at scale; the total inference and integration cost per task is a small fraction of a warehouse worker's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | placeholder |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Speech-to-text and document OCR products work reliably in production, and basic work-order parsing systems exist, but end-to-end assignment generation with correct equipment/material interpretation has material error rates in real warehouse settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | placeholder |
Sort cargo before loading and unloading.
35CI 35–35 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Sort cargo before loading and unloading.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is primarily in large, capital-intensive operations (mega-warehouses, parcel hubs) and remains pilot-stage for heterogeneous cargo. Most freight, stock, and material moving occurs in small to mid-sized facilities with lower digitization and slower AI uptake. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and logistics are adopting robotics and automation, but adoption is uneven, capital-intensive, and concentrated in large operators rather than broad and fast across the sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision systems can assist workers by identifying cargo type, destination, and sorting rules in real time, potentially speeding manual categorization. However, the physical manipulation remains human-centered, so augmentation impact is limited to information support rather than transformative productivity gain. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted scanning, routing software, and inventory systems can help workers sort more efficiently by providing guidance on cargo destination and prioritization, though physical handling remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Sorting cargo involves physical manipulation and visual inspection in real-world, variable conditions. While AI vision can categorize items, end-to-end automation including retrieval, placement, and handling of diverse cargo shapes/weights remains beyond current robotic deployment at scale; current systems would require extensive setup and human fallback. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical sorting of cargo requires perception, mobility, and manipulation that current general-purpose AI cannot perform end-to-end without specialized robotics; only narrow, structured warehouse contexts see automation.itica |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal licensing requirement exists for cargo sorting; human contact is not mandated by regulation. However, workplace safety standards, liability for damaged goods, and organizational preference for flexible human workers in mixed environments create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical workspace constraints, variable cargo types/sizes, and liability for damaged goods create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized sorting robots and their integration costs are currently higher than paying warehouse laborers for manual sorting. The infrastructure, maintenance, and custom programming needed to handle diverse cargo types makes the total cost exceed typical loaded wages for this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic sortation systems require significant capital investment in hardware and integration, often exceeding the cost of hourly laborers unless deployed at very high volume and scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic sorting exists in controlled environments (e.g., parcel facilities with conveyor systems) but deployed solutions are narrow in scope and require substantial infrastructure modification. Real-world cargo sorting—varied dimensions, fragility, irregular shapes—exceeds reliable performance of off-the-shelf AI+robotics in production at cost. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some automated sortation systems (conveyor scanners, robotic arms) exist in large distribution centers, but they are narrow-purpose hardware solutions, not general AI products, and most freight/cargo sorting is still manual. |
Attach identifying tags to containers or mark them with identifying information.
33CI 30–35 · exposure 20 · augmentation 38 · importance 4.1/5 · click for rater detail
Attach identifying tags to containers or mark them with identifying information.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Large logistics and manufacturing firms are investing in warehouse automation, but hand-tagging remains prevalent in small and mid-sized operations. Adoption is pilot-heavy rather than production-deep across the broader labor market. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and logistics are physical, lower-digitization sectors where AI/robotics adoption for granular hand-labor tasks like tagging remains in early pilot stages. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted label design or barcode generation provides minor productivity support, but the core physical task of attaching and marking offers limited scope for meaningful human-AI collaboration that raises worker output significantly. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled handheld scanners, label-generation software, and voice-directed picking systems assist workers in identifying and printing correct tags, improving speed and accuracy while the human still applies them. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While barcode generation and label printing are automatable, the physical task of attaching tags to containers and marking them requires robotic manipulation of variable physical objects. Current AI systems cannot reliably perform this full end-to-end task across diverse container types and environments at sufficient speed to meet the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | Physically attaching or marking tags on containers requires manual dexterity and mobility that current AI systems cannot perform without robotic embodiment, which is not widely deployed for this task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human labor for this task, but organizational friction is moderate: many warehouses still rely on hand-tagging due to the upfront capital cost and customization needed for variable workflows. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical environments, variable container shapes/sizes, and mixed logistics workflows create moderate operational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial automation for tagging (robotic arms, vision systems, label applicators) involves high capital and integration costs. For most hand-labor contexts, the all-in cost per task remains comparable to or exceeds the loaded wage of a low-skill laborer. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automated tagging/labeling systems (e.g., robotic arms with label printers) exist but require significant capital investment, making them costlier than human labor for most small-to-mid volume operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic bin picking and label application exist in controlled warehouse settings, but deployed systems are narrow in scope and require significant setup for specific container geometries. Production deployment remains limited and error rates are material for variable, real-world conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mature product exists that autonomously tags or marks physical containers in warehouse/freight settings at scale; this remains largely a manual task with barcode/label printers assisting but not replacing the physical handling. |
Move freight, stock, or other materials to and from storage or production areas, loading docks, delivery vehicles, ships, or containers, by hand or using trucks, tractors, or other equipment.
28CI 18–38 · exposure 13 · augmentation 38 · importance 4.0/5 · click for rater detail
Move freight, stock, or other materials to and from storage or production areas, loading docks, delivery vehicles, ships, or containers, by hand or using trucks, tractors, or other equipment.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Logistics and large warehouse operations are adopting autonomous systems, but uptake is concentrated in scale-intensive, high-tech firms (e.g., e-commerce, large 3PLs). Most small and medium freight operations continue to rely on hand labor, showing mixed and uneven adoption across sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and logistics are adopting robotics and automation, but this is capital-intensive and slow-moving compared to information-sector AI adoption; most physical labor remains manual. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted equipment (e.g., augmented forklifts with route planning, IoT-guided picking) can boost worker efficiency in well-structured environments, reducing fatigue and improving coordination. However, augmentation is most effective in digitized facilities; in many informal or small-scale operations, human workers see limited AI assistance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven route optimization, inventory management, and robotic assistance (e.g., exoskeletons, AGVs) can somewhat improve efficiency, but the core physical moving task itself sees limited AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While autonomous systems (robots, forklifts) can move materials in highly structured warehouses, this task involves significant variability—navigating unpredictable spaces, handling fragile or irregularly shaped items, adapting to different loading constraints, and responding to real-time coordination needs. Current AI-controlled equipment cannot reliably replace human workers across the typical range of conditions without major infrastructure redesign. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, mobility, and adaptability to varied environments that current AI/robotics cannot perform end-to-end at scale off-the-shelf.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Liability for autonomous systems damaging goods or injuring workers creates hesitation; OSHA and workplace safety regulations impose requirements on automated equipment but do not mandate human presence. Organizational friction and preference for on-demand human flexibility are moderate barriers, though not strong legal restrictions on automation itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical infrastructure constraints, safety regulations for equipment operation, and liability for damaged goods create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous forklifts and robots for material handling remain expensive to purchase, integrate, and maintain, with significant upfront capital and ongoing operational costs. For low-skill, minimum-wage labor in many contexts, human labor is still more cost-effective on a per-task basis, especially when flexibility and adaptability are required. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical automation (robots, AGVs) requires large capital investment in hardware and facility redesign, generally more expensive than human labor for flexible, unstructured material handling. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Autonomous material-handling robots exist in controlled environments (e.g., Amazon warehouses, structured logistics), but deployment is limited to simplified, pre-mapped spaces and standardized items. In production, these systems require extensive customization and human oversight. General-purpose autonomous freight moving at human-level reliability across diverse real-world docks and facilities remains research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | While warehouse robotics exist for narrow, structured tasks (e.g., pallet movement in optimized facilities), no deployed product broadly handles hand-loading of diverse freight across docks, vehicles, and ships reliably. |
Pack containers and re-pack damaged containers.
24CI 18–30 · exposure 8 · augmentation 25 · importance 3.3/5 · click for rater detail
Pack containers and re-pack damaged containers.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of automated packing in smaller freight operations remains minimal despite decades of robotics development. Large fulfillment centers have adopted some automation, but hand packing remains dominant in the broader logistics sector, indicating slow overall velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and logistics are adopting automation gradually, mostly for standardized, high-volume tasks, but hand-packing of irregular or damaged containers remains largely manual with slow uptake. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI and robotics offer limited real-time assistance to hand packers—vision systems could identify optimal packing layouts or flag fragile items, but such augmentation tools are not widely deployed and provide marginal productivity gains compared to human intuition. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven inventory or routing systems can indirectly support packing decisions, but there is minimal direct AI assistance for the physical act of packing or repairing damaged containers. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Packing containers involves physical manipulation of items into constrained spaces, requiring dexterous robotic systems that can handle variable geometries, weights, and fragility. Current AI vision and robotic arms exist but struggle with the adaptability and speed needed to match human efficiency at 50% time savings on diverse packing scenarios. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to handle varied items and damaged packaging, which current AI (software/models) cannot perform; robotics exist but are not general-purpose AI systems suitable for this varied task today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automated packing itself, though integration into existing warehouse workflows and safety compliance add friction. Customer preference and legacy processes slightly protect human roles, but these are not hard barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for packing, but physical workspace constraints, liability for damaged goods, and lack of robotic dexterity create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic packing systems have high upfront capital costs (equipment, integration, maintenance) and ongoing operational expenses that exceed the loaded wage of hand laborers in most settings, particularly for variable, low-volume packing operations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic packing systems capable of handling irregular and damaged containers require expensive hardware, integration, and maintenance far exceeding the cost of a manual laborer for this flexible task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While robotic packing systems exist in controlled warehouse settings, deployed products remain narrow in scope and require significant pre-staging. No mature off-the-shelf AI system reliably packs arbitrary containers at the speed and flexibility this task demands across general warehouses. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously packs and re-packs damaged containers at scale; robotic packing solutions remain narrow, item-specific pilots rather than general production deployments. |
Attach slings, hooks, or other devices to lift cargo and guide loads.
19CI 10–28 · exposure 13 · augmentation 25 · importance 4.0/5 · click for rater detail
Attach slings, hooks, or other devices to lift cargo and guide loads.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption is minimal in this sector; warehousing and freight operations remain labor-intensive and fragmented, with many small and mid-size firms relying on manual labor. The physical, real-time nature of the work and heterogeneous cargo types limit the appeal of automation investments relative to low-wage human workers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manual material handling and freight labor is a low-digitization, physical-labor sector with minimal AI/robotic adoption for this specific rigging task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI and robotics offer limited augmentation today; while some guidance systems or load-planning software might help workers optimize sling placement, the core manual task of physically attaching and guiding loads remains largely unaugmented by current AI tools. Productivity gains would be modest relative to the fundamental human skill required. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with load-planning software or sensor-based guidance systems, but it offers little direct assistance to the physical act of attaching slings and hooks. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robots can perform some aspects of rigging and load attachment in controlled warehouse environments, the task requires spatial reasoning, judgment about cargo integrity, and real-time adjustments based on load characteristics. Current AI systems lack the embodied dexterity and contextual understanding needed to reliably attach slings to varied cargo types and guide heavy loads without human supervision, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of rigging equipment on variable cargo in unstructured environments, which current AI systems cannot perform end-to-end; robotics for generalized rigging attachment is not deployable today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Moderate barriers exist: workplace safety regulations (OSHA, load testing requirements) and liability for improper rigging create some friction, though no explicit licensing requirement mandates human sign-off. Organizations face pressure to maintain human oversight and may be cautious about automation in safety-critical lifting operations. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Rigging and load-securing often falls under workplace safety regulations (e.g., OSHA) requiring trained personnel, and liability for dropped/improperly secured loads creates moderate friction against unproven automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The hardware cost for a capable robotic rigging system (specialized arms, sensors, safety systems) plus integration and ongoing maintenance significantly exceeds the loaded hourly wage of a hand laborer, especially when factoring in downtime and the need for human backup. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this specific physical task, so any hypothetical automation would require expensive custom robotics far exceeding human labor cost for this narrow function. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Limited deployed automation exists for this task; some specialized robotic systems handle rigging in narrow, controlled settings (e.g., identical boxes in automated facilities), but no mainstream product reliably attaches slings to diverse cargo or guides complex loads independently. Most deployments remain experimental or require extensive human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercially deployed product autonomously attaches slings/hooks and guides loads in warehouse or dock settings; this remains a manual, dexterity-intensive physical task. |
Maintain equipment storage areas to ensure that inventory is protected.
15CI 15–15 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Maintain equipment storage areas to ensure that inventory is protected.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Warehousing and logistics sectors show early robotics adoption for sorting and retrieval, but autonomous maintenance and protective monitoring of storage areas remains largely manual and unautomated. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Warehousing and material handling remain low-digitization, physically-intensive sectors with slow uptake of AI/robotics for general storage maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-based monitoring systems (cameras, sensors) could flag equipment or inventory damage requiring human attention, but only modestly augment the physical maintenance and protective actions the worker must still perform. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Inventory tracking software and sensors can help monitor storage conditions and flag issues, offering modest assistance, but the core maintenance work itself is not meaningfully augmented by AI. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical inspection and organization of storage areas, manual arrangement of inventory, and adaptive decision-making about protective measures—capabilities entirely outside current AI systems' scope without full robotic embodiment and real-time environmental adaptation. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical organization, cleaning, and securing of storage areas—actions requiring embodied manipulation that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements, the physical nature of the work and ongoing need for human judgment about inventory condition and storage adaptations create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but the physical nature of moving/organizing materials and equipment creates a practical barrier to pure software-based automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The infrastructure cost of deploying a mobile robot with manipulation and inspection capabilities far exceeds the hourly wage of a hand laborer, with significant integration and maintenance overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so AI cost is not comparable to human labor cost for this specific work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs autonomous maintenance of physical storage areas or inventory protection; this demands embodied, mobile robotics with environmental understanding that remains research-stage in general contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically maintains storage areas; this remains a manual labor task with no commercial robotic solution at scale. |
Carry needed tools or supplies from storage or trucks and return them after use.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail
Carry needed tools or supplies from storage or trucks and return them after use.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This occupation is in laggard sectors (construction, manual logistics, small distribution centers) with low digitization and slow robot adoption; most cargo movement remains human-performed. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manual material handling occurs in low-digitization, physically demanding sectors (construction, warehousing, general labor) where robotic/AI adoption for this specific task remains minimal and slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Minor augmentation possible through AI-optimized inventory routing or material-tracking systems that assist a human mover, but no AI significantly boosts the core manual carrying and retrieval work itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no direct assistance to a human physically carrying tools and supplies between storage and trucks. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation and transport of objects in unstructured environments, including retrieval from storage and trucks—capabilities well beyond current AI systems without specialized robotics and autonomous mobility in real-world conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation and transport task requiring locomotion, grasping, and navigation in unstructured environments—current AI (software) cannot perform this at all without embodiment, and general-purpose mobile manipulator robots are not deployed for this off-the-shelf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for automation, workplace safety regulations, unstructured physical environments, and the need for reliable operation in variable conditions create modest friction against full deployment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human do this, but physical workplace variability, safety concerns, and lack of infrastructure for robots create moderate practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current autonomous mobile robots with manipulation arms are capital-intensive ($100k–$500k+) with high integration costs, making them far more expensive than low-wage manual labor for most hand-movement and retrieval tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic hardware capable of this kind of flexible carrying/fetching is expensive to acquire, integrate, and maintain compared to low-wage manual labor, making AI/robotics costlier per task today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous material transport at scale in general warehouse or jobsite environments; existing robotics are narrow-domain (controlled factory floors) and not in production for this use case. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mature deployed product performs generalized carrying of arbitrary tools/supplies between storage and trucks in typical work sites; warehouse robotics exist only in narrow, structured contexts, not this general task. |
Assemble product containers or crates, using hand tools and precut lumber.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.2/5 · click for rater detail
Assemble product containers or crates, using hand tools and precut lumber.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs primarily in low-digitization sectors (warehousing, logistics, agriculture) with small firms and high physical-world demands, showing slow AI adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Warehousing and manual material handling sectors show low AI/robotics adoption for fine manual assembly tasks, remaining a laggard sector for this kind of physical automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI systems offer minimal productivity assistance for manual hand-tool assembly work; the task is largely manual execution with little room for software-based augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no meaningful assistance to a worker physically assembling containers with hand tools, as this is a manual craft task outside AI's software-based capabilities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Physical assembly of containers using hand tools and lumber requires dexterous manipulation in unstructured environments; current AI robotics cannot reliably perform this end-to-end without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual assembly task requiring dexterity, tool handling, and spatial manipulation of physical materials that current AI systems cannot perform without embodied robotics, which are not generally available for this application. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard licensing barriers exist, but physical task execution, workplace safety standards, and organizational inertia create moderate friction to automation adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement mandates a human for crate assembly, but physical workspace constraints and lack of robotic infrastructure create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized assembly robots capable of this task remain expensive to deploy, integrate, and maintain compared to paying minimally-trained manual laborers for the work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic solution deployed at scale for this task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial system reliably assembles product containers from precut lumber with hand tools at production quality and speed today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs container/crate assembly with hand tools and lumber in production; this remains outside the scope of software-based AI and requires advanced robotics not commercially deployed for this task. |
Connect electrical equipment to power sources so that it can be tested before use.
14CI 5–24 · exposure 8 · augmentation 25 · importance 2.9/5 · click for rater detail
Connect electrical equipment to power sources so that it can be tested before use.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption is minimal because this task occurs in logistics, warehouses, and manufacturing—sectors with high physical handling requirements and low baseline automation of manual dexterity tasks. No meaningful production deployment of robotic systems for this specific task is evident. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Warehousing and material-handling sectors show low digitization and slow adoption of AI/robotics for fine physical manipulation tasks like this one. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide marginal assistance through digital work instructions, safety checklists, or equipment identification, but the core physical task leaves little room for AI to meaningfully amplify human productivity. Augmentation is limited to procedural guidance rather than active task support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide minor assistance such as checklists, safety reminders, or diagnostic guidance for testing procedures, but it doesn't materially change the physical connection process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves physical manipulation of electrical equipment and connectors in real-world environments, which current AI systems cannot perform end-to-end. While AI could assist with identifying correct connection procedures or safety checks, the actual connection and physical handling remains beyond the capabilities of generally deployed robotics at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to identify connectors, align plugs/terminals, and physically connect equipment to power sources, which is far outside the scope of current AI systems without specialized robotics.9, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant regulatory, safety, and liability barriers exist: electrical work and equipment testing often require licensed personnel, OSHA compliance, and insurance coverage. Liability for improper connections creates asymmetric error costs that discourage automation without human oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically restricts this task, but basic electrical safety practices and workplace protocols create some procedural friction, though not a hard legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of acquiring, maintaining, and deploying robotic systems capable of safely connecting electrical equipment substantially exceeds the loaded wage of manual laborers performing this task, particularly for the diversity of equipment types encountered. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any hypothetical robotic solution would carry far higher capital and integration costs than simply having a laborer do it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform this task in production environments. While experimental robotic arms exist, they lack the dexterity, adaptability, and safety certification required for electrical equipment handling at scale in industrial settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose product performs freeform physical connection of electrical equipment to power sources in warehouse/logistics settings; this remains a manual task performed by human laborers. |
Install protective devices, such as bracing, padding, or strapping, to prevent shifting or damage to items being transported.
11CI 5–18 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Install protective devices, such as bracing, padding, or strapping, to prevent shifting or damage to items being transported.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Freight, logistics, and warehouse sectors continue to rely primarily on human labor for task-specific material handling; automation adoption remains limited to rigid, high-volume conveyor and stacking systems, not adaptive protective installation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and freight handling sectors show slow, uneven automation adoption for manual physical tasks, with robotics pilots existing but limited penetration into fine-motor securing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance through visual inspection or flagging high-risk items requiring extra protection, but the core manual installation task itself does not benefit meaningfully from current AI assistance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning load configurations or suggesting securing methods via software, but it offers little direct assistance to the physical act of installing bracing or straps. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires fine motor control, spatial reasoning, and physical manipulation of items in unpredictable configurations. Current AI systems cannot perform physical assembly or installation work in real-world environments end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of straps, padding, and bracing on physical objects in variable warehouse/vehicle contexts—no off-the-shelf AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety, liability, and worker compensation frameworks create strong organizational and legal friction around automating physical handling of cargo. Employers bear risk for damage or injury caused by inadequate securing, creating disincentive to fully replace human judgment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but physical dexterity, judgment about load variability, and lack of robotic infrastructure create practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics capable of this task would be prohibitively expensive to purchase, program, and maintain relative to the hourly wage of hand laborers performing this work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic alternative performing this physical task, so any hypothetical automation would require expensive custom robotics far exceeding human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems reliably perform adaptive protective device installation on diverse cargo types in production settings today. The task demands real-time adjustment to item geometry, weight distribution, and material properties that current automation cannot handle consistently. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs physical securing devices; robotic manipulation for this task remains research/pilot stage at best, not production reality. |
Adjust controls to guide, position, or move equipment, such as cranes, booms, or cameras.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.1/5 · click for rater detail
Adjust controls to guide, position, or move equipment, such as cranes, booms, or cameras.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption in freight and material handling remains minimal despite decades of automation investment; most operations still rely on certified human operators. Physical environments, safety requirements, and regulatory frameworks slow any transition to autonomous control. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manual material handling and equipment operation sectors show low digitization and slow AI adoption compared to information/professional services sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Some camera systems and automated positioning feedback exist to assist operators, but current AI augmentation in real-time equipment control is limited. Most assistance remains in the form of sensors and mechanical aids rather than AI decision support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some assistive technologies (e.g., camera-assisted guidance, sensor alerts) can help operators improve precision, but this remains a minor augmentation of a physically manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Adjusting controls for cranes, booms, or cameras requires real-time spatial reasoning, safety assessment, and fine motor coordination in dynamic physical environments. Current AI systems cannot reliably operate industrial equipment in unpredictable settings without human oversight and intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical manipulation of heavy equipment in dynamic environments, which current AI systems cannot perform end-to-end without embodiment and robust real-world perception/actuation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Equipment operation is heavily regulated—most jurisdictions require licensed human operators (crane operator certification, for example) to legally control industrial equipment. Liability exposure for AI-caused accidents is substantial, creating hard legal and insurance barriers. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Operating heavy equipment like cranes typically requires certification/licensing and carries significant liability for accidents, creating strong regulatory and safety barriers to automation without human oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI hardware (sensors, actuators, real-time processing), integration, safety certification, and continuous human oversight for equipment control exceeds the wages of trained equipment operators, especially for small to mid-scale operations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized robotic/automated crane systems require large capital investment, engineering, and maintenance far exceeding the cost of a human operator for typical variable freight-handling tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While some research exists in robotics and teleoperation, no deployed product reliably performs autonomous equipment control for freight/material handling at production scale. Real-world deployments still require licensed human operators for safety and liability reasons. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose product autonomously operates cranes or booms for freight handling; existing autonomous crane systems are narrow, fixed-installation research or specialized industrial pilots, not general hand-labor equivalents. |
Related occupations — Transportation & Material Moving
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.