Grinding and Polishing Workers, Hand
51-9022.00Median wage $42,660/yr10,510 employed (US)Rank #412 of 923 scored · top 45% by substitution
Grind, sand, or polish, using hand tools or hand-held power tools, a variety of metal, wood, stone, clay, plastic, or glass objects. Includes chippers, buffers, and finishers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution29
Exposure21
Augmentation32
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
17 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
6%
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%24
panel mean rating 2.0/5 → substitution pressure 24/100
Technical feasibility todayw 20%17
panel mean rating 1.7/5 → substitution pressure 17/100
Cost vs. human wagew 15%20
panel mean rating 1.8/5 → substitution pressure 20/100
Adoption barriersw 20%inverted — strong barriers lower the score65
panel mean rating 2.4/5 (barrier strength) → substitution pressure 65/100
Sector adoption velocityw 10%16
panel mean rating 1.6/5 → substitution pressure 16/100
Task breakdown (17 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 and processing data on specified forms.
81CI 65–97 · exposure 83 · augmentation 75 · importance 3.9/5 · click for rater detail
Record product and processing data on specified forms.
81| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and quality-assurance sectors show robust adoption of automated data capture, computer vision inspection logging, and form-filling systems in production, particularly in industries with high digitization. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Small-scale metal finishing and hand-polishing operations are typically low-digitization, low-margin shops that adopt new digital workflows slowly compared to information-sector firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists workers by auto-populating forms from sensor data or images, reducing manual transcription burden and error rates while the worker verifies and monitors quality control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Even where full automation isn't implemented, AI-enabled form tools, voice-to-text, and auto-validation significantly speed up and reduce errors in a worker's data recording process. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Recording data on forms is a highly structured, rule-based task that current OCR, computer vision, and form-filling automation systems handle routinely. AI can extract product/processing parameters and populate specified fields end-to-end with >50% time savings at equal accuracy. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording standardized product/processing data on forms is a structured data entry task that current AI, especially combined with OCR, mobile input, or digital forms integrated with sensors, can largely automate with equal or better accuracy. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory barriers exist for automating data entry itself; oversight and QA requirements add minor friction, but no licensing or human-sign-off mandate prevents substitution in most jurisdictions. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing or legal requirement for a human to manually record this data; it's a routine clerical/quality-control task with minimal regulatory protection. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven data capture and entry costs pennies per record, while manual hand-recording by a worker carries full loaded wages; cost differential easily exceeds an order of magnitude. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated data logging (barcode scanners, tablets, IoT sensors feeding databases) is inexpensive relative to manual clerical time once implemented, though initial system integration adds cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature products for data entry automation, form filling, and optical character recognition are deployed at scale in manufacturing and quality-control workflows today, with proven reliability in production environments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Manufacturing execution systems (MES) and digital form-capture tools exist and are deployed in some plants, but many hand-grinding/polishing shops still use paper logs and lack integrated digitization, so reliability varies widely across sites. |
Mark defects, such as knotholes, cracks, and splits, for repair.
61CI 35–87 · exposure 58 · augmentation 63 · importance 4.4/5 · click for rater detail
Mark defects, such as knotholes, cracks, and splits, for repair.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and woodworking sectors have rapidly adopted automated defect detection systems in recent years, with vision-based quality control now common in medium-to-large production facilities. Adoption is particularly deep in high-volume, digitized manufacturing environments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manual grinding/polishing trades are a low-digitization, physical-labor sector where AI/automation adoption is slow and mostly limited to large-scale manufacturing rather than small shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can assist workers by highlighting suspected defects in real-time on displays or overlays, allowing workers to focus on borderline cases and confirming automaton decisions. This keeps humans in the loop while substantially raising throughput and consistency. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted defect detection cameras can help workers spot flaws faster, but the marking and judgment on repair priority still rely heavily on human inspection. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Computer vision systems can reliably detect surface defects like knotholes, cracks, and splits on wood or similar materials, and mark them for repair with high accuracy. This task is well-suited to image classification and object detection models that achieve well over 50% time savings compared to manual inspection. |
| Task automatability | claude-sonnet-5 | 2/5 | Computer vision can detect surface defects like knots and cracks in controlled imaging setups, but marking requires physical interaction with varied materials and lighting conditions typical of hand-based inspection, limiting full automation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human inspection for marking defects in manufacturing. Primary barriers are integrating vision systems with existing production lines and organizational inertia, but these are relatively soft obstacles in modern manufacturing. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but quality-control liability and need for physical marking action create some organizational friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-based defect detection has minimal marginal cost per inspection once deployed, while hand inspection requires sustained labor. Inference and system maintenance costs are orders of magnitude lower than the loaded wage of a grinding and polishing worker performing continuous inspection. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Vision-based inspection hardware and integration costs are significant relative to a low-wage manual task, making all-in AI costs often comparable to or higher than human labor unless deployed at very high volume. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Machine vision systems are deployed in woodworking and manufacturing plants to detect surface defects, though integration with marking/flagging systems varies. Products exist and perform this task reliably in controlled environments, though real-world variability in material finish and lighting can introduce occasional errors. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated visual inspection systems exist in some manufacturing lines (e.g., lumber grading), but general-purpose deployed products that both detect and physically mark defects across varied hand-inspected materials are narrow and not widespread. |
Verify quality of finished workpieces by inspecting them, comparing them to templates, measuring their dimensions, or testing them in working machinery.
47CI 28–67 · exposure 45 · augmentation 63 · importance 4.6/5 · click for rater detail
Verify quality of finished workpieces by inspecting them, comparing them to templates, measuring their dimensions, or testing them in working machinery.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Automated inspection is increasingly common in high-volume manufacturing and electronics, but adoption remains uneven across small job shops and craft production. Pilots are widespread in production environments but full displacement is sector-dependent. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding and polishing is a low-digitization, small-shop-heavy trade with limited AI/automation adoption compared to information or finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted inspection tools can highlight anomalies, measure dimensions in real time, and guide operators to problem areas, substantially increasing human inspector throughput and reducing error rates while the human remains the final arbiter of quality. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted vision tools and digital calipers/measurement software can help workers verify dimensions faster and flag anomalies, improving inspection speed and consistency. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Machine vision systems and automated measurement devices can perform dimensional inspection, comparison to templates, and detect surface defects at scale with >50% time savings. However, testing in working machinery may require human judgment and setup, limiting full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 2/5 | Automated visual/dimensional inspection systems exist but require significant setup for specific parts and often lack the flexibility of manual functional testing in machinery, so full end-to-end automation with equal quality is not yet standard for this varied task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some manufacturing processes have regulatory or quality documentation requirements that may demand human sign-off or verification. Customer specifications and liability concerns around quality can create friction, though no hard legal barrier prevents automation of the inspection itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this inspection task, though liability for defective parts reaching customers creates some incentive to retain human final sign-off in critical applications. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Vision inspection systems and automated gauging equipment have dropped significantly in cost and can operate continuously with minimal labor, making the per-unit inspection cost substantially lower than manual inspection by hand workers over time. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Vision inspection hardware and integration costs can be substantial relative to a worker's wage for small-batch or varied production, making cost savings marginal unless volume is high. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Automated optical inspection (AOI) and coordinate measurement machines (CMMs) are widely deployed in manufacturing, but they typically handle specific, controlled environments and may struggle with complex geometries or edge cases that require human expertise. Production systems exist but with material limitations in scope. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Machine vision inspection systems are deployed in some high-volume manufacturing settings, but hand grinding/polishing shops often produce varied, low-volume parts where such systems are not commonly integrated. |
Study blueprints or layouts to determine how to lay out workpieces or saw out templates.
29CI 23–35 · exposure 20 · augmentation 50 · importance 4.4/5 · click for rater detail
Study blueprints or layouts to determine how to lay out workpieces or saw out templates.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manual grinding and polishing is a mature, low-digitization trade with aging workforce in traditional manufacturing. Adoption of AI-based planning is minimal; most shops still rely on experienced workers' judgment and printed blueprints rather than digital systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding and polishing is a low-digitization, physical trade sector with minimal AI adoption for layout tasks currently observed. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD-based layout assistance and blueprint visualization tools can help workers organize their approach and reduce planning time, but the task itself is relatively straightforward once experience is gained, so augmentation value is moderate rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD interpretation or digital blueprint annotation tools can help workers understand specifications faster, offering moderate productivity gains while the human still performs the physical layout. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Blueprint interpretation and spatial layout planning require visual understanding of technical drawings and translating them into physical workpiece positioning. Current AI can analyze images and generate layout suggestions, but the task demands precise spatial reasoning, material-specific constraints, and real-time adjustment to actual workpiece conditions that typically require human verification and manual correction. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision models can interpret simple blueprints but translating that into physical layout marking on a workpiece requires spatial reasoning and manual dexterity not achievable end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements for layout planning itself, workplace safety standards and the integration with union-represented skilled trades create organizational friction. The worker's tacit knowledge and on-site judgment act as soft barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational friction and reliance on skilled tradespeople for precise manual layout work create some resistance to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A skilled grinding worker's hourly labor is relatively modest, and the AI system cost (including specialized vision training, integration with CAD systems, and ongoing maintenance) would likely exceed the cost savings from automating this planning stage alone. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even if AI could assist interpretation, the physical layout and template sawing still requires human labor and equipment, so cost savings are minimal relative to the human wage for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While computer vision systems can read blueprints and 2D layout software exists, no deployed product reliably interprets technical drawings and automatically determines optimal workpiece layouts for grinding/polishing without human oversight. Systems exist at research or narrow-application stages but lack the robustness needed for production substitution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs blueprint-to-physical-layout translation for hand grinding/polishing work; CAD/CAM automation exists in adjacent CNC contexts but not for this manual task as described. |
Transfer equipment, objects, or parts to specified work areas, using moving devices.
29CI 23–35 · exposure 25 · augmentation 13 · importance 3.4/5 · click for rater detail
Transfer equipment, objects, or parts to specified work areas, using moving devices.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing shops, especially grinding and polishing firms, are typically small to mid-size with lower digitization and slower automation adoption. Robotics deployment in these sectors remains limited; most operations still rely on manual transfer methods. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding and polishing occurs largely in small-to-midsize manufacturing shops, a sector with historically low digitization and slow robotics adoption relative to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Transfer of objects using moving devices is inherently a manual task with little opportunity for AI to assist while a human remains in the loop. AI cannot meaningfully augment the physical act of moving equipment in a grinding shop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Basic conveyors or carts can assist with moving items, offering some efficiency gain, but this is more mechanical assistance than AI-driven augmentation of the worker's judgment or skill. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical manipulation and transfer of objects to specified locations requires spatial reasoning, dexterity, and real-time environmental navigation that current AI-controlled systems struggle with. While autonomous mobile robots exist, integrating them for diverse object types and work environments remains unreliable, and no off-the-shelf system achieves the 50% time-saving threshold for general task completion. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical transfer of parts to work areas can be done by AGVs/AMRs or conveyors in structured environments, but 'hand' grinding/polishing implies varied, often small-batch or irregular material handling not fully automatable end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical safety liability is substantial: a malfunctioning transfer system could damage expensive parts, harm nearby workers, or contaminate precision work areas. Regulatory oversight of workplace automation, worker compensation exposure, and organizational resistance to replacing flexible human labor create meaningful adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical workspace layout, safety zoning for moving equipment, and capital cost create real organizational friction to automating this specific handling step. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic transfer systems require significant capital investment, integration costs, and facility modification. For a grinding and polishing worker performing varied transfers across a workshop, the all-in cost of a deployed robotic system typically exceeds the loaded wage of a human worker. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AGVs and conveyor systems require significant capital investment, integration, and maintenance that often exceeds the marginal cost of a human moving parts short distances, especially in low-volume shops. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some robotic systems can transfer objects in highly controlled, structured environments (e.g., warehouse picking), but real-world grinding and polishing workshops have variable object sizes, fragility concerns, and spatial constraints that current deployed systems handle unreliably. Production deployments are narrow and task-specific, not generalizable. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Mobile robots and automated material handling exist in some manufacturing settings, but deployment for this specific hand-finishing context is narrow and not broadly reliable across varied part shapes and workstations. |
Move controls to adjust, start, or stop equipment during grinding and polishing processes.
28CI 21–35 · exposure 20 · augmentation 25 · importance 4.2/5 · click for rater detail
Move controls to adjust, start, or stop equipment during grinding and polishing processes.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is concentrated in large-scale, high-volume manufacturing (automotive, aerospace) where robotic polishing is cost-justified; many small and medium job shops still rely on skilled hand workers, indicating slow overall sectoral adoption relative to information-intensive occupations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and hand-finishing trades are historically slow adopters of AI-driven automation compared to office/information sectors, though some CNC/robotic grinding adoption exists in larger firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted feedback systems (computer vision for surface inspection, predictive maintenance alerts) offer limited augmentation, but the core manual control task—feel of the workpiece, pressure modulation, speed adjustment—remains difficult to enhance without full automation of the motor control loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based sensors or monitoring could assist with process optimization or predictive maintenance, but direct assistance to a human manually operating grinding controls is limited today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While starting/stopping equipment is technically automatable, real grinding and polishing requires constant adjustment of position, pressure, and speed based on visual feedback and tactile sensing of the workpiece. Current AI lacks the fine motor control and adaptive sensing to reliably manage the continuous micro-adjustments needed to maintain quality, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manual control task requiring hand-eye coordination and real-time tactile feedback on a physical workpiece; current AI cannot perform the physical manipulation itself, only potentially guide automated machinery.dynamics |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers for the equipment operation itself, workplace safety regulations, quality inspection requirements, and the need for human judgment on surface finish create moderate friction. Additionally, many small job shops lack the infrastructure and capital for automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical workplace integration, safety certification for automated machinery, and capital/organizational friction create moderate barriers to swapping in robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Industrial robotic systems capable of polishing operations cost hundreds of thousands of dollars in capital and integration, plus ongoing maintenance and calibration, making them far more expensive than a skilled hand-grinder's loaded wage for most applications. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting manual grinding stations with robotic control systems is capital-intensive and often costlier than retaining a human operator, especially for low-volume or variable-part work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production system today reliably performs the integrated task of monitoring a grinding/polishing process and making real-time control adjustments to maintain surface quality. Robotic polishing exists in narrow, high-volume manufacturing contexts but requires extensive setup and human oversight; it does not constitute reliable end-to-end task execution in general. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Robotic/CNC grinding systems exist in production but require capital investment and reprogramming per part; general-purpose AI does not operate hand controls on legacy manual equipment. |
Load and adjust workpieces onto equipment or work tables, using hand tools.
26CI 15–38 · exposure 13 · augmentation 13 · importance 4.2/5 · click for rater detail
Load and adjust workpieces onto equipment or work tables, using hand tools.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large manufacturing facilities have adopted robotic arms and conveyors for workpiece handling, but small and medium shops—where hand grinding and polishing remain common—lag significantly. Adoption is sectoral: mature in high-volume automotive and electronics, slow elsewhere. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding and polishing is a manual manufacturing trade with low digitization and slow automation adoption, typically only automated in high-volume standardized production, not general hand-finishing work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers limited assistance for a fundamentally manual, tactile task. Vision systems can guide placement, but the hand adjustment and real-time feedback loop remains almost entirely human-driven; augmentation gains are modest. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance for the physical act of loading and adjusting workpieces using hand tools, as this is a manual, tactile task outside AI's capability. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Positioning and securing diverse workpieces with hand tools requires dexterous manipulation and real-time adjustment to fit irregularly shaped parts. Current robotic systems struggle with variable geometries and fine tactile feedback needed for consistent, repeatable placement without damage. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of workpieces and hand tool use, a manual dexterity task not addressable by current software-based AI systems; robotic automation exists but is distinct from 'AI' as generally deployed and requires custom engineering per part. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Manufacturing shops face no strong regulatory or licensing barriers to automating workpiece loading. Primary friction comes from capital investment requirements and the need to reprogram or adjust fixtures when part types change frequently. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical variability in workpieces, safety requirements around machinery, and the cost of custom tooling create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Custom robotic setups for workpiece handling are expensive to implement and maintain, often costing hundreds of thousands of dollars. The loaded labor cost for a hand worker is modest relative to integration, programming, and oversight expenses for robotic alternatives. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Deploying robotic loading systems requires significant capital investment, custom fixturing, and engineering, making it more expensive than a human worker for most low-to-medium volume grinding/polishing operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While robotic arms exist in manufacturing, they typically require fixturing and pre-programmed positions for standardized parts. No deployed system reliably handles the ad hoc adjustment and problem-solving inherent in positioning arbitrary workpieces with hand tools at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No general-purpose AI product loads and adjusts physical workpieces onto equipment; this remains a physical robotics challenge with only narrow, highly customized industrial solutions in limited deployment. |
Measure and mark equipment, objects, or parts to ensure grinding and polishing standards are met.
24CI 14–35 · exposure 20 · augmentation 38 · importance 4.5/5 · click for rater detail
Measure and mark equipment, objects, or parts to ensure grinding and polishing standards are met.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Hand grinding and polishing remains concentrated in small, local shops and mid-size manufacturers with low digitization. Adoption of advanced automated measurement is slow; most shops still rely on manual measurement by experienced workers, reflecting low capital investment and sector-wide technology lag. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding and polishing is a low-digitization manual trade with minimal AI/robotics penetration in small-batch or artisanal production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted measurement tools (image capture with automated dimension detection, AI-suggested mark locations) can speed up planning and reduce some measurement steps, but require human verification and physical execution. The technology offers modest productivity gains while the worker retains primary responsibility. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital calipers, laser measurement tools, and simple vision-guided systems can assist with precision measurement, but marking and standard verification remain largely manual with limited AI-driven productivity gains. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect dimensions and marks, the task requires precise physical measurement and marking on varied, irregular equipment and parts in real-world conditions. Current systems struggle with the 3D geometry, material variation, and accuracy tolerance needed for manufacturing standards without human oversight and physical manipulation. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of tools and measuring instruments on physical objects, which current AI systems cannot perform end-to-end without robotic embodiment; vision-based measurement could assist but not replace the physical marking step.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Quality assurance in manufacturing is often subject to regulatory oversight (ISO, aerospace, automotive standards), and liability falls on the organization if automated measurement fails to catch defects. Responsibility and sign-off requirements typically require human expertise, limiting full substitution even where automation is technically feasible. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, but precision quality control and liability for defective parts create some organizational caution before removing human inspection/marking. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated vision + marking systems require significant capital investment (cameras, robots, calibration), integration labor, and maintenance. For small-batch or custom work typical of grinding/polishing, the per-task cost remains higher than a skilled human worker using calipers and marking tools. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic or automated measurement/marking systems for varied small-batch parts require expensive custom tooling and integration, making them costlier than a skilled worker's marginal task time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision can identify surfaces and patterns in lab settings, but production systems that reliably measure and mark physical parts across diverse geometries and materials remain rare. Existing deployed solutions are limited to controlled, repetitive shapes; the task's variability in equipment and object types exceeds current reliable automation scope. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose product performs physical measuring and marking of hand-finished parts in production hand-polishing environments; this remains a manual craft task. |
Remove completed workpieces from equipment or work tables, using hand tools, and place workpieces in containers.
23CI 15–30 · exposure 8 · augmentation 0 · importance 4.2/5 · click for rater detail
Remove completed workpieces from equipment or work tables, using hand tools, and place workpieces in containers.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Grinding and polishing facilities, particularly small to mid-sized job shops and specialized manufacturers, remain relatively low-digitization environments with slow capital equipment turnover. Adoption of robotic removal/placement is piecemeal, limited to high-volume, standardized production lines rather than the generalist task described. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding and polishing is a low-digitization, small-scale manufacturing sector with slow automation adoption historically, particularly for flexible physical material-handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | There is minimal augmentation potential for AI or robotics in this purely physical manual task; the worker already operates efficiently with direct sensorimotor feedback and judgment about workpiece condition and placement. No digital tool meaningfully assists the human in removing and placing parts. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to a human performing this direct physical task of removing and placing workpieces. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves variable spatial handling, positioning, and containment of diverse workpiece geometries using hand tools. Current robotic systems struggle with the unstructured nature of workpiece removal and placement without significant task-specific setup, making consistent 50% time savings unlikely across typical shop environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterous handling of finished workpieces with hand tools; current AI (software/LLMs) cannot perform physical actions, and general-purpose robotics is not yet at this reliability/cost point for widespread deployment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no regulatory or licensing barriers to automating this manual removal task, but organizational friction exists: shops vary widely in layout and workpiece types, and workers' familiarity with equipment creates inertia. Safety considerations around workpiece placement add modest friction but not hard barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but workplace safety, equipment variability, and capital cost create practical friction against automation adoption. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic grasping systems suitable for handling variable workpieces are expensive to deploy and maintain, while hand removal by workers remains labor-intensive but cost-effective for small-batch or mixed-part operations. The integration and oversight costs for flexible automation are typically higher than the loaded wage of a grinding/polishing worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Deploying robotic arms, tooling, vision systems, and integration for this narrow unloading task typically costs far more than the marginal human labor cost, especially for variable workpieces and small-batch environments common in hand grinding/polishing shops. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While robotic arms and collaborative robots exist in some manufacturing settings, deployed systems rarely handle the ad-hoc removal and placement of diverse completed workpieces from general equipment with production reliability. General-purpose systems cannot reliably detect, grasp, and safely place variable parts without extensive engineering per production line. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mature, widely deployed product performs this exact unstructured pick-and-place-with-tools task reliably in production manufacturing settings at scale; robotic bin-picking exists in narrow, engineered cells but not as a general solution for hand tool removal of workpieces. |
File grooved, contoured, and irregular surfaces of metal objects, such as metalworking dies and machine parts, to conform to templates, other parts, layouts, or blueprint specifications.
23CI 10–35 · exposure 13 · augmentation 38 · importance 3.7/5 · click for rater detail
File grooved, contoured, and irregular surfaces of metal objects, such as metalworking dies and machine parts, to conform to templates, other parts, layouts, or blueprint specifications.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains slow in the broader grinding and polishing sector, especially among small to mid-size job shops and contract manufacturers that dominate the industry. Large aerospace and automotive plants have invested in automated systems, but the sector as a whole lags information and finance in AI/robotics penetration. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metalworking and hand-finishing trades are low-digitization, physical-labor sectors with minimal AI agent adoption for hands-on tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted vision overlays, tool-path optimization software, and robotic jigs can meaningfully aid a skilled filer by suggesting contours, detecting surface flaws, and automating repetitive strokes on simpler sections, raising output quality and speed while the worker retains judgment on complex or unusual geometries. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with generating precise blueprint specifications, tolerances, or 3D template comparisons, but offers little direct help with the physical filing action itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Filing irregular metal surfaces requires real-time sensory feedback (haptic, visual), adaptive tool pressure, and spatial reasoning to conform to templates or blueprints. Current AI systems lack the embodied dexterity and continuous error correction needed to handle contoured and grooved surfaces reliably end-to-end, though robotic arms could assist in limited, repetitive scenarios. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, tactile hand-filing task requiring fine motor control and real-time judgment on physical metal parts; no AI system can perform physical manipulation of metal objects. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Moderate barriers exist: quality assurance and final inspection are often human-sign-off requirements, liability for out-of-tolerance parts rests with the manufacturer, and many job shops operate as small businesses with low capital budgets for automation. However, no legal licensing requirement bars automation, and some manufacturers do substitute robots in high-volume runs. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for hand-filing, but the physical nature of the task itself is the primary barrier rather than regulation—AI software simply cannot act on physical objects. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A robotic filing system with vision guidance, maintenance, and operator oversight is expensive to deploy and integrate. The labor cost of skilled grinding/polishing workers is moderate, and the all-in AI cost (hardware, integration, error recovery) currently approaches or exceeds the human wage for most job shops and small fabrication settings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI software has no mechanism to perform physical filing, so there is no viable AI cost comparison; any automation would require robotic hardware, not generally available AI systems. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While robotic arms and vision systems exist for grinding and polishing tasks, reliable production systems that autonomously file complex irregular surfaces to tight blueprint tolerances remain rare and require extensive setup. Deployed systems handle simpler, more uniform surfaces; this task's variability in grooves and contours keeps it largely semi-automated or operator-controlled. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs hand-filing of metal surfaces; this remains a purely physical craft skill not addressed by AI products, only by robotics/CNC which is a different automation category. |
Sharpen abrasive grinding tools, using machines and hand tools.
19CI 15–24 · exposure 8 · augmentation 13 · importance 3.5/5 · click for rater detail
Sharpen abrasive grinding tools, using machines and hand tools.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Grinding and polishing shops are typically small, physical-work-dependent operations with low digital infrastructure. These sectors show slow AI adoption overall, with little evidence of robotic tool-sharpening deployment in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manual trades and hands-on manufacturing tasks like this show minimal AI/robotic adoption, being physical, low-digitization work with little penetration of AI agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with measurement and diagnostics (detecting tool wear via imaging) or process control, but the core manual sharpening task is difficult for AI to augment meaningfully while the worker remains in the loop. Assistance exists at the margins, not the core. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to a worker physically sharpening grinding tools with hand and machine tools. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Sharpening grinding tools requires precise manipulation of physical objects, sensory feedback (feel, sound, visual inspection), and judgment about tool geometry—capabilities where current AI excels less. While some preparatory steps (measurement, setup) could be automated, the hands-on sharpening work itself remains difficult for autonomous systems to perform reliably end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hand-eye coordination and tactile judgment to sharpen abrasive tools; no current AI system can perform this physical manipulation end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no strict licensing or legal requirements to perform tool sharpening, but quality control standards and accountability for tool performance create moderate friction. Organizations may prefer human expertise to avoid downtime from poor automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but the physical nature of the task and need for specialized dexterous manipulation create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of tool sharpening are expensive in capital, setup, and maintenance, while sharpening is often performed by a single skilled worker with minimal overhead. The cost per sharpened tool would likely exceed the worker's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any AI-related cost comparison is moot; a human worker remains the only practical option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs end-to-end tool sharpening autonomously. The task demands real-world physical dexterity, tool positioning, and quality assessment that exceed what production robotic systems can do consistently in unstructured workshop settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs this physical tool-sharpening task; robotics for this specific fine-motor, judgment-based work remains research-stage or nonexistent at product level. |
Grind, sand, clean, or polish objects or parts to correct defects or to prepare surfaces for further finishing, using hand tools and power tools.
19CI 15–23 · exposure 5 · augmentation 25 · importance 4.5/5 · click for rater detail
Grind, sand, clean, or polish objects or parts to correct defects or to prepare surfaces for further finishing, using hand tools and power tools.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is concentrated in high-volume, standardized manufacturing (automotive, aerospace) with predictable geometries; smaller shops and custom work remain labor-intensive. Overall adoption velocity is slow outside mass production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing hand-finishing trades are a low-digitization, physical-labor sector with minimal AI/robotic agent adoption in production for flexible, small-batch finishing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered vision systems can assist with defect detection and quality inspection, but current systems offer limited real-time guidance for hand-tool adjustment or surface-finish feedback during the grinding and polishing process itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with defect detection (vision-based quality inspection) to guide where grinding/polishing is needed, but it does not meaningfully augment the physical execution of the task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise tactile feedback, visual inspection of defects, and adaptive hand-tool manipulation in variable physical environments. Current AI and robotics cannot reliably perform this end-to-end with 50% time savings at equal quality on diverse materials and defect geometries without extensive manual intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring tactile feedback, dexterity, and force control on varied surfaces/defects; current AI systems (including robotics) cannot perform this end-to-end reliably outside narrow, fixtured industrial settings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical safety regulations and workplace standards apply, but no licensing requirement mandates human performance. Organizational friction and the need for adaptive, dexterous manipulation create practical barriers to substitution, though not legal ones. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but quality control, safety (power tools, dust, injury liability), and the need for adaptive judgment on defect correction create moderate organizational friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Industrial grinding/polishing robots are capital-intensive and require programming, integration, and supervision; total cost per part typically exceeds the loaded wage for skilled hand-workers on diverse, non-repetitive work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic/automated finishing systems require expensive fixturing, sensors, and integration per part type, making them costlier than a human worker for variable, small-batch hand-finishing work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized industrial robots exist for grinding/polishing narrow, standardized geometries in controlled settings, but they lack the adaptability to handle defect detection, material variation, and surface quality judgment that human workers apply. Deployed systems remain narrow and require significant setup per part type. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No general-purpose deployed AI product performs hand grinding/sanding/polishing across varied parts; existing robotic finishing cells are narrow, pre-programmed, and require heavy engineering for specific parts, not flexible AI-driven hand tool use. |
Select files or other abrasives, according to materials, sizes and shapes of workpieces, amount of stock to be removed, finishes specified, and steps in finishing processes.
19CI 5–33 · exposure 13 · augmentation 38 · importance 4.0/5 · click for rater detail
Select files or other abrasives, according to materials, sizes and shapes of workpieces, amount of stock to be removed, finishes specified, and steps in finishing processes.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing sectors employing hand grinding and polishing workers tend to be small-shop, craft-oriented operations with low digitization. Adoption of AI systems in these contexts remains minimal, with most operations relying on experienced workers' tacit knowledge rather than algorithmic guidance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding and polishing is a manual trade in manufacturing with low digitization and minimal AI adoption reported in this specific sub-task domain. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by cataloging abrasive properties, recommending options based on material and workpiece inputs, and organizing process knowledge. However, the task remains highly dependent on the worker's experience and tactile feedback, so augmentation would be partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with recommending abrasive types via lookup systems or specifications databases, but it offers minimal help with the physical selection and handling itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Selecting abrasives requires evaluating multiple material properties, workpiece geometry, and process parameters. While AI could assist in suggesting abrasives based on input specifications, the task involves tactile inspection, spatial judgment, and domain expertise that current systems cannot reliably perform end-to-end without significant human oversight and iterative feedback. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical selection and handling of tools based on tactile and visual assessment of physical workpieces, which current AI systems cannot perform without robotic embodiment far beyond off-the-shelf capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: manufacturing standards and certifications often require human sign-off on material selection; liability for surface finish defects creates strong organizational friction; and regulatory requirements in industries like aerospace or medical device manufacturing mandate documented human expertise in critical finishing steps. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically for this task, but the physical nature and need for tactile judgment on varied workpieces create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Setting up an AI system to reliably recommend abrasives would require extensive training data, domain expert validation, and ongoing human oversight. The cost of integration and error-correction would likely exceed the wage of a skilled worker making these selections, especially given the high cost of poor abrasive choices in manufacturing. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical selection task, so any AI-based approach (e.g., robotic vision system) would require far more capital investment than the human wage it replaces for low-volume, high-variability work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed production system reliably selects grinding/polishing abrasives autonomously. Computer vision systems can classify materials and rough sizes, but determining optimal grit, type, and sequence for finishing requires specialized domain knowledge and real-time feedback that remains out of reach for current off-the-shelf AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously selects abrasives/files for hand grinding and polishing operations in production settings; this remains a manual craft skill. |
Trim, scrape, or deburr objects or parts, using chisels, scrapers, and other hand tools and equipment.
17CI 10–24 · exposure 8 · augmentation 25 · importance 4.4/5 · click for rater detail
Trim, scrape, or deburr objects or parts, using chisels, scrapers, and other hand tools and equipment.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Grinding and polishing is concentrated in small- to medium-sized manufacturing and job-shop environments with limited digitization; adoption of automated deburring remains slow and confined to high-volume, standardized parts in larger facilities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand finishing and manufacturing trades are a low-digitization, physical-labor sector with minimal AI/robotic agent adoption at present. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with defect detection or marking surfaces for removal via computer vision, but the core manual execution—feel, judgment, and tool control—remains human-driven; augmentation opportunities are narrow relative to the task's tactile and adaptive demands. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven vision systems or robotic arms can assist with defect detection or repetitive path planning, but current tools offer limited direct assistance to a worker actively performing hand-tool trimming and scraping. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Trimming and deburring require precise tactile feedback, visual inspection, and adaptive hand-tool manipulation in varied physical contexts. Current AI systems lack the dexterity and real-time sensorimotor feedback needed to reliably perform these operations end-to-end, though partial automation of marking or planning stages is conceivable. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, dexterous physical task requiring hand-eye coordination and tactile feedback on varied part geometries; no current AI system (software or robotic) can perform this end-to-end reliably today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there is no licensing requirement to automate hand-tool work, quality control standards, liability for surface damage, and the prevalence of custom, low-volume parts create organizational and practical friction to full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but physical/organizational friction (capital cost, part variability, need for fixturing) meaningfully slows substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic deburring systems are capital-intensive and require significant integration and programming labor, making their all-in cost per task substantially higher than a trained grinding worker's loaded wage for most small-to-medium shop operations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Deploying robotic automation for varied, low-volume hand-finishing work requires costly custom tooling, fixturing, and programming that exceeds the cost of a human worker for most applications. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs manual deburring and scraping with hand tools at production scale. Robotic systems exist but require extensive setup and struggle with part variability, workpiece positioning, and the judgment-heavy aspects of trimming decisions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | Robotic deburring exists only in narrow, highly engineered, fixed-part production lines, not as a general deployed product replicating flexible hand-tool trimming/scraping across varied parts. |
Apply solutions and chemicals to equipment, objects, or parts, using hand tools.
17CI 10–24 · exposure 8 · augmentation 13 · importance 3.6/5 · click for rater detail
Apply solutions and chemicals to equipment, objects, or parts, using hand tools.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a manual, physical task in manufacturing and maintenance—sectors with low AI adoption velocity and where companies have been slow to deploy dexterous robotics at scale due to high setup costs and task variability. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manual manufacturing and hand-finishing trades are among the slowest sectors for AI adoption, with minimal digitization or agentic deployment in this specific physical task domain. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by predicting optimal chemical concentrations or recommending tool selection, but the core execution—hand application—remains human-dependent, offering limited productivity uplift. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no direct assistance to a worker physically applying chemicals with hand tools, as the task is tactile and non-informational in nature. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves physical manipulation of hand tools and precise application of chemicals to objects, requiring dexterity and spatial reasoning in unstructured environments. Current AI lacks the embodied manipulation and sensorimotor capability to reliably apply solutions by hand across diverse object geometries at anywhere near 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical task requiring dexterous hand-tool manipulation and tactile judgment of surfaces; no current AI system can perform this end-to-end without robotic hardware, which is not the AI system itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers to automation, the task involves chemical handling and safety compliance, worker preference for human skill verification, and the need for operator oversight to prevent damage or accidents create meaningful organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, but physical workspace constraints, equipment variability, and quality-control needs create moderate organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of this task would require significant hardware investment, specialized grippers, and extensive programming per application context, far exceeding the loaded wage cost of a skilled hand worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since no viable AI/robotic solution exists for general deployment, the human worker remains the only practical and cost-effective option; any robotic alternative would require expensive custom engineering exceeding labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can reliably pick up hand tools and apply chemicals to objects with the precision and safety this task demands. This remains firmly in the research domain for robotic manipulation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs hand-tool application of solutions/chemicals to objects; this remains a physical manufacturing task outside software AI's scope, and robotics for such fine manual work is research-stage at best. |
Clean brass particles from files by drawing file cards through file grooves.
15CI 15–15 · exposure 0 · augmentation 0 · importance 1.6/5 · click for rater detail
Clean brass particles from files by drawing file cards through file grooves.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Grinding and polishing is a traditional, low-digitization manufacturing craft that remains concentrated in small job shops with minimal automation adoption, making adoption of specialized AI-driven cleaning systems unlikely. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hand grinding/polishing trades are low-digitization, small-scale physical work environments with minimal AI or robotics adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | The task is purely mechanical maintenance work with no meaningful role for AI assistance; a human simply removes debris by hand using a file card, leaving no augmentation opportunity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for this simple, manual, tactile cleaning subtask. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of file cards through grooves in a precise, repetitive manner to remove brass particles. Current AI and robotic systems lack the specialized tactile feedback and fine motor control needed to reliably perform this narrow manual operation at a cost that would justify automation. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine-manipulation physical task requiring dexterous handling of a file card against grooves; no off-the-shelf AI or robotic system performs this today with any meaningful time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no licensing or regulatory barriers to automation, and no human-contact or liability requirements, but the specialized nature and low volume of work creates natural organizational friction against automation investment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory barrier exists, but the physical, low-value nature of the task creates practical friction against investing in automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of building a robotic system capable of reliably drawing file cards through grooves would far exceed the loaded wage of a grinding and polishing worker performing this routine cleaning task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any automation would require custom robotics and sensing far exceeding the cost of a worker spending seconds cleaning a file, making AI/robotic solutions far more expensive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product performs this specific task of cleaning files with cards. The task is too specialized and low-volume to have attracted automated solutions, and it remains performed manually in niche manufacturing contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs this specific hand-tool maintenance task; it remains purely manual in real workshops. |
Repair and maintain equipment, objects, or parts, using hand tools.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Repair and maintain equipment, objects, or parts, using hand tools.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Grinding and polishing workers operate primarily in manufacturing, small shops, and maintenance sectors that adopt automation slowly. These are often smaller firms with physical, hands-on work environments where AI/robotic adoption remains minimal and mainly at the research or pilot stage. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manual trades and hands-on manufacturing/repair sectors show minimal AI/robotics adoption for unstructured physical repair tasks, reflecting low digitization and physical embodiment challenges. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with diagnostic guidance (e.g., suggesting repair procedures via image analysis), but current systems offer limited real-time support while a human performs manual hand-tool work. The value of such assistance is modest given the task's emphasis on tactile, real-time problem-solving. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, manuals, or troubleshooting guidance, but offers little direct enhancement to the physical hand-tool repair action itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Repairing and maintaining equipment with hand tools requires fine motor control, tactile feedback, spatial reasoning, and real-time problem diagnosis that current AI cannot perform end-to-end. While robots exist for some manufacturing tasks, the versatility and adaptability needed for hand tool-based repair across diverse objects places this well beyond current AI capability. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of tools and materials to repair objects, which current AI systems cannot perform end-to-end; robotics for fine manual repair work remains research-stage.no off-the-shelf system replicates this. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety liability for faulty equipment repairs is high; customers and organizations typically require human accountability and certification. Regulatory frameworks often mandate human inspection and sign-off on critical repairs, creating strong legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing typically required for hand repair work, but the physical dexterity, judgment, and variability of tasks create strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robots capable of manipulation with hand tools are extremely expensive to acquire and program, while skilled repair workers command moderate wages. The capital and integration costs far exceed what a human repair technician would cost per task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any hypothetical robotic solution would be far more costly than a skilled human worker for this narrow, variable task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product today can autonomously diagnose and repair equipment using hand tools in real-world conditions. Existing robotic systems are narrowly scoped to specific repetitive tasks and lack the dexterity, sensorimotor feedback, and reasoning required for general repair work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs hand-tool-based physical repair and maintenance of arbitrary equipment/parts reliably in production settings today. |
Related occupations — Production
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.