Packers and Packagers, Hand
53-7064.00Median wage $36,280/yr559,820 employed (US)Rank #177 of 923 scored · top 19% by substitution
Pack or package by hand a wide variety of products and materials.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution40
Exposure30
Augmentation30
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
12 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
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%31
panel mean rating 2.2/5 → substitution pressure 31/100
Technical feasibility todayw 20%30
panel mean rating 2.2/5 → substitution pressure 30/100
Cost vs. human wagew 15%32
panel mean rating 2.3/5 → substitution pressure 32/100
Adoption barriersw 20%inverted — strong barriers lower the score76
panel mean rating 2.0/5 (barrier strength) → substitution pressure 76/100
Sector adoption velocityw 10%31
panel mean rating 2.2/5 → substitution pressure 31/100
Task breakdown (12 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Record product, packaging, and order information on specified forms and records.
78CI 72–84 · exposure 75 · augmentation 63 · importance 4.2/5 · click for rater detail
Record product, packaging, and order information on specified forms and records.
78| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Large logistics, retail, and manufacturing sectors have already deployed automated data capture systems in production; smaller firms are rapidly adopting barcode/RFID infrastructure. This is among the fastest-adopted automation in warehousing. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Logistics and warehousing have moderate digitization with growing WMS/barcode adoption, but many hand-packing operations remain small-scale or low-tech, slowing uniform adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Barcode-scanning tools and form-auto-fill systems meaningfully assist hand-packers by reducing manual typing and lookups, raising productivity. However, the task is straightforward enough that augmentation is incremental rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where full automation lags, mobile scanning apps and voice-assisted data entry significantly speed up and reduce errors in the human recording process. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording structured product, packaging, and order information onto forms is highly automatable via OCR, barcode scanning, and data entry automation systems. Current AI can extract and input this data with >50% time savings when integrated with warehouse management systems, though some edge cases (handwritten notes, non-standard formats) may require human intervention. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording structured product/order/packaging data onto forms is a data-entry task that AI-enabled scanning, OCR, and warehouse management systems can largely automate with barcode/RFID integration and digital forms. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates a human perform data entry; warehouse practices are employer-controlled. Main friction is organizational (legacy systems, change management, worker displacement concerns) rather than regulatory or contact-based. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, liability, or human-contact requirement tied to recording packaging/order data; it's a low-stakes clerical function with minimal regulatory protection. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Barcode scanners, OCR, and automated data entry systems cost pennies per transaction, while a hand-packer's loaded wage (~$30–40k/year) amortizes to $15–20+ per hour. Automation cost-per-record is typically 1–2% of manual labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated scanning and digital record systems cost far less per transaction than manual data entry once deployed, though initial integration with existing hand-packing workflows adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed warehouse management and inventory systems routinely automate data recording through barcode/RFID scanning and automated form population; major logistics providers (Amazon, DHL) have production systems doing this at scale. Error rates on structured data entry are low, though integration with legacy systems may create friction. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Warehouse management systems, barcode scanners, and mobile data-capture apps already handle this recording function reliably in many production logistics environments today, though some manual/paper-based settings persist. |
Measure, weigh, and count products and materials.
68CI 52–84 · exposure 62 · augmentation 50 · importance 4.4/5 · click for rater detail
Measure, weigh, and count products and materials.
68| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Packing and logistics are highly digitized, capital-intensive sectors where computer vision and automated counting are already deployed at scale in major distribution centers and manufacturing lines; adoption is accelerating. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and manual packaging remain a physically-oriented, lower-digitization sector where automation adoption is real but slower and more capital-intensive than in office/information work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted vision tools can help humans inspect and count faster, but the task is primarily sensorimotor measurement—augmentation potential is real but lower than for judgment-heavy tasks requiring deep human expertise. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital scales, barcode counters, and simple sensor feedback already assist human packers in verifying counts and weights, improving accuracy and speed without replacing the worker. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI with computer vision and scale integration can reliably measure, weigh, and count items in controlled settings, delivering >50% time savings when combined with existing automation hardware. This task requires minimal judgment and fits well within the scope of industrial computer vision systems deployed today. |
| Task automatability | claude-sonnet-5 | 3/5 | Weighing and counting can be automated via sensors, scales, and vision systems, but this refers to hand packaging work where the physical measuring action itself requires robotic hardware, not just AI software, limiting pure AI automation of the task as performed by a human hand packer. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or licensing barriers exist; the task is not human-contact critical and carries low liability risk. Adoption mainly faces typical operational inertia and equipment retrofit costs rather than regulatory prohibition. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing, legal, or liability barriers preventing automation of measuring, weighing, and counting products; it's a standard industrial process improvement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Once-installed vision and weight-sensing systems cost far less per unit than sustained human labor; inference and integration costs are negligible at scale, making AI an order of magnitude cheaper over time. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Industrial weighing/counting automation can be cost-effective at scale, but for small-batch or variable-product hand packaging, integration and equipment costs may be comparable to low-wage manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (vision systems with scale integration, conveyor-line sensors, and counting algorithms) perform this reliably in production at packing facilities and warehouses, though narrow operational scope (specific product types, lighting conditions) sometimes limits plug-and-play generalization. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated checkweighers, counting scales, and vision-based counting systems are deployed in many warehouses and factories, but many hand-packing environments still rely on manual measuring due to variable product types and low-volume flexibility needs. |
Load materials and products into package processing equipment.
53CI 35–71 · exposure 50 · augmentation 25 · importance 4.0/5 · click for rater detail
Load materials and products into package processing equipment.
53| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, logistics, and e-commerce sectors show strong measured adoption of robotic loading systems; pilot and production deployments are common in high-volume operations and larger firms. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and warehousing are adopting automation steadily but unevenly; many packing operations remain manual due to cost and flexibility needs, placing this in a slower-adoption physical-labor sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Augmentation potential is limited; robotic systems displace rather than assist human loaders, and the task offers minimal opportunity for AI-assisted human productivity gains in the traditional sense. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven vision or coordination systems can somewhat assist by optimizing loading sequences or flagging errors, but they don't substantially enhance the core physical loading motion performed by the worker. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Robotic systems can reliably load materials and products into equipment with consistent motion and high throughput; this is largely repetitive, spatial positioning work that meets time-saving thresholds in many deployment contexts today. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manipulation task requiring perception and dexterity to place varied items into equipment; current general-purpose AI (LLMs, vision models) cannot perform the physical loading itself, though robotic systems exist for narrow, structured cases.atab This requires embodied robotics, not just software AI, limiting the automation bar today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist to automating physical loading; adoption is primarily constrained by equipment cost and line throughput requirements rather than licensure or liability asymmetry. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement for a human to perform this task, but physical workspace constraints, product variability, and capital costs create real practical friction to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Robotic loading systems require significant upfront capital (equipment, integration, maintenance) offsetting labor savings; cost advantage depends on volume and labor rates, making the ratio roughly comparable to human packers in many scenarios. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Industrial robotic loading systems require significant capital investment, integration, and maintenance, often costing more than low-wage hand packing labor unless at very high volume and long amortization periods. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed robotic arms and bin-picking systems are in production at scale in warehouses, distribution centers, and manufacturing plants; they handle structured loading tasks reliably, though performance degrades with irregular item shapes or fragile goods. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Robotic loading/packaging systems are deployed in some high-volume, standardized production lines, but for the general 'hand packer' role with variable materials, mature reliable robotic replacement is not widespread. |
Obtain, move, and sort products, materials, containers, and orders, using hand tools.
48CI 18–79 · exposure 38 · augmentation 25 · importance 4.1/5 · click for rater detail
Obtain, move, and sort products, materials, containers, and orders, using hand tools.
48| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Warehouse and fulfillment sectors (e-commerce, logistics, pharma distribution) have aggressive automation roadmaps; major players like Amazon and DHL deploy robotic picking and sorting at scale, demonstrating fast, deep production adoption in digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and manufacturing sectors are adopting robotics and automation, but adoption is slow, capital-intensive, and mostly limited to large-scale, high-volume operations rather than broad, deep displacement of hand packaging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While collaborative robots can assist human packers, the task itself—routine sorting and movement—offers limited upside to human productivity; augmentation is mainly handling edge cases or quality checks rather than transforming the core workflow. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-guided systems (e.g., pick-to-light, sorting algorithms, route optimization) can somewhat improve efficiency in coordinating human packers, but the core physical handling task itself sees limited direct AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | The core subtasks—obtaining, moving, and sorting items using hand tools—are highly repetitive and rule-based. Robotic arms and mobile manipulators can perform these operations end-to-end with clear time and labor savings, meeting the ≥50% bar in many warehouse settings, though the requirement to use existing hand tools slightly reduces the ceiling. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical manipulation of diverse objects in unstructured environments, which current AI systems and robotics cannot perform end-to-end with major time savings; it's a physical manual labor task, not a digital/cognitive one. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist; no licensing is required, and liability falls on the operator. Adoption is primarily constrained by capital cost and setup friction in small-to-medium facilities, not by structural legal impediments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers, but physical workspace constraints, variability of materials, and need for dexterity create practical organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Robotic systems, especially for high-volume repetitive packing and sorting, achieve operational costs (depreciation, electricity, maintenance) that are an order of magnitude below loaded human labor ($20–30/hour) in most developed markets. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Industrial robotic packing/sorting systems require large capital investment, integration, and maintenance costs that generally exceed the low wages typical of hand packaging labor, especially for low-volume or variable tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Warehouse automation systems (robotic arms, sortation systems, automated guided vehicles) are in production at major fulfillment centers and logistics hubs; they reliably perform item obtaining, moving, and sorting at scale. Maturity is high, though some narrow edge cases (fragile items, irregular shapes) still require human intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose product reliably performs hand-tool-based physical sorting and moving of varied products at scale; even advanced warehouse robotics (e.g., Amazon's) handle narrow, controlled subsets and still require significant human labor for irregular items. |
Examine and inspect containers, materials, or products to ensure that product quality and packing specifications are met.
45CI 35–55 · exposure 38 · augmentation 63 · importance 4.5/5 · click for rater detail
Examine and inspect containers, materials, or products to ensure that product quality and packing specifications are met.
45| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Automated vision inspection is piloted and deployed in food, pharmaceutical, and manufacturing sectors, but adoption remains unevenly distributed; many smaller operations still rely on manual inspection, indicating middling velocity relative to information-sector automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Packing and warehousing sectors are adopting automation (conveyor vision systems, robotics) but overall AI-driven adoption in hand-packing environments remains slow and physically constrained compared to office/knowledge sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can effectively highlight suspect items for human review, accelerate screening workflows by pre-filtering good units, and reduce inspector fatigue—materially raising productivity while the human makes final judgment calls, especially on borderline or unfamiliar defects. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered vision or sensor tools can flag likely defects or measurement deviations, helping human packers focus attention faster, though humans still verify and handle ultimate quality decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI vision systems can detect many common defects and packaging compliance issues (e.g., label placement, visible damage, weight verification), but require significant setup for product-specific criteria and struggle with nuanced quality judgments or novel defect types that human inspectors handle routinely. |
| Task automatability | claude-sonnet-5 | 2/5 | Basic visual defect detection can be automated with machine vision, but hand packers' inspection often involves varied products, tactile checks, and ad hoc judgment calls that current general AI systems cannot fully replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automating inspection tasks, though supply-chain contracts and customer expectations for human quality sign-off create some organizational friction; liability for missed defects creates mild caution but is not a hard legal bar to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but liability for shipping defective/mislabeled products and the need for physical presence to handle diverse items creates moderate organizational friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inspection systems have moderate capital and integration costs comparable to labor, but ongoing inference and human oversight to validate AI decisions partially offset savings, yielding roughly comparable total cost of ownership to hand inspection in many settings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying vision systems, sensors, and integration engineering for a low-wage manual task is often costlier upfront than the wage cost it replaces, especially for smaller-scale or variable packing operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Computer vision quality inspection systems are deployed in production by some manufacturers, but typically require extensive training data per product type, have material false-positive/negative rates, and perform best on standardized, well-lit scenarios—limiting scope to simpler inspection tasks. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Machine vision inspection systems exist and are deployed in some high-volume manufacturing lines, but they are narrow, product-specific, and not a generalized 'inspect any container/material' solution usable by a typical hand-packing operation. |
Mark and label containers, container tags, or products, using marking tools.
34CI 33–35 · exposure 25 · augmentation 38 · importance 4.0/5 · click for rater detail
Mark and label containers, container tags, or products, using marking tools.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hand packing and labeling remains concentrated in small to medium facilities, warehouse environments with low digitization, and industries with high product variety—sectors that adopt automation more slowly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Hand packaging and warehousing sectors are relatively slow adopters of advanced automation and AI compared to information/professional services, though conveyor-based labeling automation is common in high-volume settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Vision-assisted systems and AI-generated label templates can assist workers in confirming correct labels and placement, and automated barcode generation improves speed, but the core physical task still requires human execution in most settings. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with generating labels, printing barcodes, or verifying label placement via vision systems, but it offers limited direct augmentation to the physical act of marking/tagging performed by hand. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate labels and use simple robotic systems to apply them, the task requires precise spatial positioning, handling of varied container shapes, and reading context to place marks correctly—most production environments still rely on human packers for this variability. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manipulation task requiring hand-eye coordination to mark, label, or tag physical items, which current AI systems cannot perform without robotic embodiment; only the decision of what to print/label could be automated, not the physical application. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent automation, but physical handling requirements, need for real-time quality judgment, and organizational investment in manual processes create moderate friction to adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automating labeling, though physical workspace integration, equipment cost, and handling variability create moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic marking systems are capital-intensive and require integration costs that often exceed the loaded wages of low-cost hand packing labor in many regions, making AI solutions more expensive for small to medium volumes. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Dedicated labeling/tagging machinery can be cost-effective at high volume, but for the flexible, low-volume, or variable-item hand-packing context this task describes, deploying robotics or vision-guided systems is costlier than paying manual labor per unit. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision systems exist for label verification, and some industrial robots can apply pre-positioned labels, but reliable end-to-end marking of arbitrary containers at production speed without human intervention remains limited to narrowly controlled environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated labeling machines exist and are widely deployed, but these are traditional industrial automation (label applicators, printers) rather than AI systems; AI-driven robotic labeling for varied hand-packing contexts remains narrow and non-standard. |
Place or pour products or materials into containers, using hand tools and equipment, or fill containers from spouts or chutes.
33CI 30–35 · exposure 25 · augmentation 25 · importance 4.0/5 · click for rater detail
Place or pour products or materials into containers, using hand tools and equipment, or fill containers from spouts or chutes.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is concentrated in large, capital-intensive food and beverage plants; small and medium packaging operations remain largely manual, reflecting slow overall velocity in a fragmented, low-digitization sector with high changeover costs. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and warehousing are physical, moderately digitized sectors; automation adoption is steady but slow and typically limited to large-scale, high-volume operations rather than widespread deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance to hand packers; vision systems could inspect or sort, but the core manual placement and pouring task itself provides little opportunity for AI augmentation while the human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven vision systems or robotic arms can assist with certain repetitive filling tasks, but for most hand packaging work, there is limited meaningful software-based augmentation of the human performing the task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some high-volume, uniform packing lines use robotics, the task requires precise hand placement and sensitivity to product fragility that current general-purpose AI systems cannot reliably execute end-to-end without extensive task-specific engineering. Hand-tool operation and adaptive pouring from varied sources remain beyond 50% time-saving threshold for off-the-shelf AI. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manipulation task requiring dexterity and adaptation to varied products/containers; current AI (software) cannot perform it, though robotics could in narrow, fixed setups. Most hand-packing environments remain too variable for full robotic substitution today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no hard licensing requirements for automation, organizational friction, setup complexity for product changeovers, and worker displacement concerns create moderate adoption friction in smaller facilities that dominate this occupation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human do this task, but physical workspace constraints, product variability, and capital costs create real adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized packing robots and supporting infrastructure (vision systems, integration, maintenance) remain capital-intensive and operationally expensive for most small and mid-size operations, often exceeding the cost of minimum-wage hand packing when full lifecycle costs are included. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Industrial filling/packing robots require significant capital investment, engineering integration, and maintenance, often exceeding the cost of low-wage manual labor unless volumes are very high and consistent. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic arms exist in specialized factory settings for repetitive packing, but deployed systems are narrowly scoped and require significant setup per product type. Current AI vision and manipulation systems lack the dexterity and real-time adaptation to handle diverse container shapes, product weights, and spout variability at production scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some deployed robotic packaging/filling systems exist for high-volume, standardized product lines, but general hand-packing across varied materials and containers is still mostly done by humans in production settings. |
Seal containers or materials, using glues, fasteners, nails, and hand tools.
31CI 26–35 · exposure 20 · augmentation 13 · importance 4.2/5 · click for rater detail
Seal containers or materials, using glues, fasteners, nails, and hand tools.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Packing and packaging occurs across low-digitization small and medium enterprises, agriculture, retail warehousing, and artisanal production. Adoption of robotic automation remains spotty and concentrated in large-scale food/beverage and manufacturing; most hand-packing environments have not yet deployed such systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehousing and manufacturing sectors adopt automation slowly relative to information sectors, with robotic packaging solutions deployed only in high-volume, standardized operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI tools offer minimal assistance to hand-packers. Conveyor systems and basic machinery augment the task, but AI guidance on sealing technique, adhesive choice, or fastener selection is not yet commonplace or proven to substantially raise individual worker productivity in typical settings. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers negligible assistance to a human physically sealing containers with hand tools, as this is a manual dexterity task outside typical AI/software augmentation scope. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Sealing containers by hand with glues, fasteners, and nails requires precise spatial positioning and variable pressure application. While some structured packing scenarios (e.g., uniform box sealing) could see partial automation, the diversity of container shapes, materials, and fastening methods, combined with the tactile feedback needed, means current AI cannot achieve ≥50% time savings end-to-end across typical packing environments. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manipulation task requiring dexterity to handle glues, fasteners, nails, and hand tools on varied materials; current AI systems (software-based) cannot perform this without embodiment via robotics, which remains limited and expensive for varied packaging.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Packing work is largely unregulated and carries no mandatory human sign-off. However, quality control, ergonomic constraints in existing facilities, and the high technical integration burden present moderate friction to adoption; many small packing operations have limited capital and digitization. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers restrict automation, but physical workspace integration, material variability, and capital costs create practical friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Industrial robotic sealing systems have high capital costs, integration expenses, and ongoing maintenance. For low-margin hand-packing work in small to medium operations, the all-in cost of automation typically exceeds the loaded wage of a packing worker, making substitution economically unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic packaging automation can be cheaper at massive scale for standardized products, but for varied hand-packing tasks the capital and integration costs for robotics exceed low-wage manual labor costs in most contexts. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic systems can perform some sealing tasks in controlled industrial settings, but no mature off-the-shelf AI product reliably handles the variability of hand-packing work (different containers, adhesive types, fastener choices). Deployed solutions are narrow and require substantial customization; they do not work reliably at scale across typical packing operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mainstream deployed AI product performs generalized hand-sealing of containers; industrial packaging automation exists but is hard-coded machinery, not AI-driven, general-purpose systems. |
Remove completed or defective products or materials, placing them on moving equipment, such as conveyors, or in specified areas, such as loading docks.
28CI 18–38 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail
Remove completed or defective products or materials, placing them on moving equipment, such as conveyors, or in specified areas, such as loading docks.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Food, beverage, and large-scale manufacturing have deployed conveyor and some robotic automation, but adoption remains uneven. Many small and medium enterprises still rely on manual hand packing, reflecting moderate rather than rapid sectoral adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | placeholder |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI and robotic systems offer limited augmentation for human hand packers; they are designed for replacement rather than assistance. A human could use a simple visual aid (defect classifier) but this is tangential to the core task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | placeholder |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While conveyor placement and product removal are mechanically feasible, current AI systems struggle with the visual discrimination of defects, variable product geometries, and the real-time physical manipulation required. Robotic systems exist but require significant customization per product type and still achieve only partial automation in practice. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring vision-guided grasping, sorting judgment, and mobility in a workspace; current AI systems cannot perform the physical action itself, only decision support."},"feasibility":{"rating":1,"rationale":"No widely deployed autonomous system reliably removes and sorts completed/defective products across varied packaging environments; robotic bin-picking remains narrow and pilot-stage."},"cost_ratio":{"rating":1,"rationale":"Robotic automation for this physical task requires expensive hardware, integration, and maintenance, making it costlier than low-wage hand packaging labor in most contexts."},"barriers":{"rating":2,"rationale":"No licensing or legal requirement mandates human performance, but physical workspace variability and quality-control judgment create moderate practical friction to automation."},"adoption_velocity":{"rating":2,"rationale":"Manufacturing and warehousing sectors are adopting automation slowly and unevenly, with robotics deployment concentrated in large-scale, high-volume facilities rather than broad-based adoption."},"augmentation":{"rating":2,"rationale":"AI-enabled vision systems can flag defective products for human removal, offering some assistance, but does not fundamentally transform this hands-on physical task."}}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing barrier exists, but physical automation requires substantial capital investment, facility retrofitting, and integration with existing conveyor systems. Organizational friction is moderate: many small and mid-sized operations lack the scale or capital to justify robot deployment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | placeholder |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robotic systems (hardware, integration, maintenance) typically cost $100k–$500k+ per station, with payback periods of several years. For low-wage packing roles ($15–$20/hr loaded), the ROI is marginal unless volumes are very high and products are standardized. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | placeholder |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic arms and vision systems for bin picking exist in production, but they work reliably only in highly controlled settings with standardized products. General-purpose hand packing automation remains limited in real-world deployment due to variability in product shape, size, and condition assessment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | placeholder |
Assemble, line, and pad cartons, crates, and containers, using hand tools.
25CI 15–35 · exposure 13 · augmentation 13 · importance 4.1/5 · click for rater detail
Assemble, line, and pad cartons, crates, and containers, using hand tools.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is slow and limited to high-volume, standardized facilities (food, pharmaceuticals); most hand packing occurs in small and medium enterprises with lower digitization and capital budgets, resulting in laggard sector characteristics overall. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand packaging occurs in warehousing/manufacturing sectors with historically low digitization and slow robotics adoption for flexible, low-margin manual tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers limited assistance for this manual dexterity task; computer vision could aid in quality inspection or material sorting, but does not meaningfully enhance the core hand-tool assembly and padding work. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI systems offer little direct assistance to a human physically assembling and padding containers with hand tools; this is not a cognitive or software-mediated task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some aspects like cardboard assembly and basic padding could be partially automated by robots, the task requires dexterous hand-tool manipulation, spatial reasoning, and adaptation to variable container sizes and materials—capabilities that current general-purpose AI/robotic systems struggle with reliably in unstructured settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to assemble, line, and pad containers with hand tools; current AI (software/LLM systems) cannot perform physical assembly work, and robotics for this remains narrow and not generally deployed off-the-shelf. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or licensing barriers exist for automation itself, but high capital costs, integration complexity, and the need to handle variable container types and materials create organizational and technical friction to deployment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but physical workspace constraints, variable materials, and the need for adaptable dexterity create moderate practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robots and custom packing automation are capital-intensive and typically cost-prohibitive for the wage levels of hand packers unless throughput is very high and standardized; most packing operations remain labor-based because human cost is lower. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized robotic packaging systems require significant capital investment, integration, and maintenance costs that typically exceed low-wage manual labor costs for this flexible, low-precision task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic packaging systems exist for high-volume, standardized tasks, but they are narrow in scope and require extensive setup; current AI-based vision and manipulation systems have not achieved reliable production-scale deployment for the flexible hand-tool work described here. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mature, widely deployed product performs generalized carton assembly, lining, and padding with hand tools; robotic packaging solutions exist only for narrow, highly standardized cases, not general hand-tool assembly tasks. |
Transport packages to customers' vehicles.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.9/5 · click for rater detail
Transport packages to customers' vehicles.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task occurs primarily in retail, logistics, and small/medium businesses with low automation investment. Current adoption of AI/robotics for last-mile package transport to vehicles is negligible in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Retail, warehousing, and curbside pickup sectors have low physical automation adoption for this specific task; robotic curb delivery remains experimental, not mainstream. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with route optimization or vehicle-location identification, but the physical transport task itself offers limited augmentation opportunities while the human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers little direct augmentation for the physical act of carrying and loading packages into a vehicle. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires navigating to customer vehicles, lifting packages, and placing them in vehicles—complex physical manipulation in unstructured environments that current robotics cannot do reliably. End-to-end automation at 50% time savings is not achievable with available systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical transport of goods to a vehicle, a manipulation and mobility task that current AI systems (software-based) cannot perform without embodied robotics, which is not deployed at scale for this specific task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While not heavily regulated, this task involves direct customer interaction and potential liability for damage during transport. Some organizational friction exists around replacing human workers, but no hard legal barrier prevents automation attempts. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but practical barriers like liability for property/vehicle damage and lack of robotic infrastructure create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous mobile manipulation systems capable of this task (if they existed) would be extremely expensive to deploy, maintain, and operate compared to human packagers working at minimum or near-minimum wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical robots capable of navigating lots and handling diverse packages are far more expensive to deploy and maintain than paying a human worker for this simple physical task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic system can autonomously transport packages to varied customer vehicles at scale in real-world conditions. This remains a research problem, not a production-deployed capability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mature deployed product autonomously carries packages to customer vehicles in typical retail/warehouse settings; robotic delivery pilots exist but are narrow and not general-purpose for this task. |
Clean containers, materials, supplies, or work areas, using cleaning solutions and hand tools.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.9/5 · click for rater detail
Clean containers, materials, supplies, or work areas, using cleaning solutions and hand tools.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of automation in manual packing and facility cleaning remains low; the work is concentrated in small to mid-sized operations with low digitization, minimal robotics investment, and high tolerance for manual labor. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand packaging and cleaning work occurs in warehouses/manufacturing—low-digitization, physical-labor sectors with minimal AI/robotic adoption for this specific subtask. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers negligible assistance to a human performing this cleaning task; the work is largely physical and improvisational, with no meaningful role for AI-based tools or advice. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to a human physically cleaning containers, materials, or a work area with hand tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning physical containers and work areas requires dexterous manipulation, spatial reasoning, and adaptability to irregular surfaces and layouts that current AI cannot reliably execute end-to-end in unstructured environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical cleaning task requiring hand-tool manipulation and mobility in a workspace; no off-the-shelf AI system can perform physical cleaning of containers or work areas today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While no strict licensing barrier exists, the task occurs in varied physical spaces with evolving conditions, creating practical friction around setting up and monitoring autonomous systems; most facilities rely on human labor due to cost and flexibility. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical workspace variability, safety requirements around cleaning chemicals, and lack of mature robotic tooling create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous cleaning robots capable of this task remain expensive ($50k+) with ongoing maintenance, far exceeding the loaded hourly wage of a hand packer, making economic substitution infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute readily deployable for this task, so any hypothetical automation (custom robotics) would be far more costly than low-wage manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems in production today perform general-purpose cleaning of containers and work areas autonomously; specialized robots exist only in narrow, controlled settings and are not yet mainstream in packing environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs general-purpose hand cleaning of containers/materials with tools; robotic cleaning solutions remain narrow, research-stage, or limited to specific industrial contexts. |
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.