Tailors, Dressmakers, and Custom Sewers
51-6052.00Median wage $41,640/yr13,920 employed (US)Rank #647 of 923 scored · top 70% by substitution
Design, make, alter, repair, or fit garments.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution21
Exposure13
Augmentation28
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
22 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
0%
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%15
panel mean rating 1.6/5 → substitution pressure 15/100
Technical feasibility todayw 20%10
panel mean rating 1.4/5 → substitution pressure 10/100
Cost vs. human wagew 15%13
panel mean rating 1.5/5 → substitution pressure 13/100
Adoption barriersw 20%inverted — strong barriers lower the score55
panel mean rating 2.8/5 (barrier strength) → substitution pressure 55/100
Sector adoption velocityw 10%6
panel mean rating 1.2/5 → substitution pressure 6/100
Task breakdown (22 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.
Sew buttonholes and attach buttons to finish garments.
54CI 33–75 · exposure 50 · augmentation 25 · importance 3.6/5 · click for rater detail
Sew buttonholes and attach buttons to finish garments.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Industrial and fast-fashion garment sectors have extensively adopted robotic buttonhole and button-attachment systems; this is standard automation in high-volume manufacturing. Adoption is fastest in large operations and slowest in small custom shops, but the overall velocity in the sector is well-established. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring and dressmaking is a low-digitization, small-scale, physical craft sector with minimal AI/robotics adoption for finishing tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Once buttonholes and buttons are attached by machine or robot, human intervention is minimal and augmentation is limited. AI vision might help with quality inspection, but the core task itself offers little scope for human-AI collaboration that raises productivity beyond full automation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers little direct assistance for the physical sewing action itself, though pattern/design software or automated equipment can support surrounding aspects of garment production. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Sewing buttonholes and attaching buttons are highly repetitive, spatially well-defined operations that can be performed by commercial sewing machines or robotic systems with vision guidance, achieving significant time savings at equal or superior quality. Current industrial automation achieves this end-to-end on garment production lines, though integration with varied garment types requires some setup. |
| Task automatability | claude-sonnet-5 | 2/5 | Buttonhole sewing and button attachment can be done by industrial buttonhole/button machines, but for custom tailoring with varied fabrics, sizes, and placements this remains largely manual and requires physical dexterity AI cannot provide end-to-end."},"rating_note":2}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement or legal mandate for human oversight exists for these operations; they are routine manufacturing tasks. The main barrier is organizational—custom tailors and small shops may lack capital or volume justification, but nothing prevents automation in principle. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human do this task, but customer expectations for hand-finished quality and small-batch/custom work create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Industrial sewing robots and button-attachment systems cost thousands to tens of thousands in capital, but amortized over large production volumes, per-garment cost is a small fraction of skilled labor wages. For lower-volume custom work, the cost ratio is less favorable, but high-volume operations strongly favor automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized industrial machines can be cost-effective at scale but require capital investment and are not economical for single-garment custom work compared to a tailor's manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Industrial robotic sewing systems and automated button-attachment machines are deployed in production facilities worldwide and perform these tasks reliably at scale. While high-volume garment manufacturers use this technology routinely, deployment for small custom operations remains less common but technically proven. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated buttonhole/button machines exist and are used in mass manufacturing, but for custom/bespoke garment finishing typical of this occupation, no AI-driven robotic system reliably performs this task in production. |
Trim excess material, using scissors.
48CI 15–81 · exposure 45 · augmentation 13 · importance 4.2/5 · click for rater detail
Trim excess material, using scissors.
48| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large-scale garment manufacturers widely use automated cutting; small custom tailor shops lag significantly in adoption due to low volume, capital constraints, and customer expectations for hand craftsmanship, resulting in uneven sectoral penetration. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a low-digitization, small-scale, highly manual trade with minimal AI/robotics adoption for physical cutting tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Automation of trimming reduces the assistive potential since the task itself is mechanical and low-skill; AI adds limited value to the human tailor's decision-making or output quality once the trim line is defined. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no direct assistance for the physical act of trimming fabric with scissors, though it may help elsewhere in pattern design or measurements. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Robotic systems with computer vision and precision cutting tools (laser cutters, automated scissors) can identify excess material edges and trim them end-to-end with significant time savings and equal or better quality. This is a well-defined, repeatable physical task without ambiguity. |
| Task automatability | claude-sonnet-5 | 1/5 | Trimming excess fabric with scissors requires fine motor dexterity, real-time visual judgment of fit and fabric behavior, and physical manipulation that current AI systems cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement for automated fabric trimming; however, custom tailoring preserves human touch as a market differentiator and some customers expect hand work, creating modest organizational and market friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific cutting action, but it requires physical dexterity and contextual judgment embedded in a broader tailoring workflow, creating practical though not regulatory barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated cutting equipment has high upfront capital cost but per-unit trimming cost is very low once amortized. For high-volume operations, AI-guided automated cutting is substantially cheaper than hand-labor; for one-off custom work, setup costs make it less favorable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this precise physical task, so any hypothetical automation setup would be far more costly than a human tailor's marginal time for this micro-task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Industrial fabric-cutting automation (laser cutters, CNC-equipped cutting systems) is deployed in garment manufacturing at scale. While some custom tailoring shops still use manual scissors, automated trimming systems exist and function reliably in production environments, though adoption varies by shop size. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or industrial product autonomously trims garment fabric with scissors in production settings; robotic fabric cutting exists mainly for flat, pre-cut industrial patterns, not custom fitting trims. |
Estimate how much a garment will cost to make, based on factors such as time and material requirements.
39CI 28–50 · exposure 33 · augmentation 38 · importance 4.0/5 · click for rater detail
Estimate how much a garment will cost to make, based on factors such as time and material requirements.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring remains a largely non-digitized, small-business-dominated sector with low overall tech adoption. Tailors and dressmakers operate independent shops or small teams without centralized inventory or labor tracking systems, making adoption of cost-estimation AI very slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a small-scale, low-digitization trade sector with little evidence of AI tool adoption for costing or estimation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by suggesting material costs from supplier databases or calculating standard labor hours for common alterations, but custom garments require tailor judgment about fit complexity and customer preferences that AI currently cannot capture well enough to materially boost productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (spreadsheets, calculators, or chatbots) can help tailors quickly estimate costs by organizing material and labor inputs, offering moderate productivity benefits while the tailor still supplies key judgment calls. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help calculate material costs and estimate labor time from patterns, the task requires judgment about efficiency, waste, complexity, and customization factors that vary per tailor. Current systems lack the embodied understanding of sewing craft to reliably estimate costs across diverse garment types and custom requests. |
| Task automatability | claude-sonnet-5 | 3/5 | Cost estimation from time and material requirements is a structured calculation task that AI could do if given accurate inputs (fabric costs, labor time, pattern complexity), but translating physical garment specifications into those inputs still requires human judgment or measurement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There are moderate barriers: tailors typically rely on their own judgment and experience-based pricing rather than external systems, and customers often expect direct consultation. However, no legal barrier exists to using AI assistance, and adoption friction is mainly organizational rather than regulatory. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human perform cost estimation; it's a business/administrative task with minimal legal barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing an AI system to estimate garment costs would require significant setup (training on shop data, material databases, labor records) and oversight. For small tailor shops, this cost infrastructure would likely exceed the savings from automating occasional estimates. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Once inputs are digitized, AI-assisted calculation is cheap, but the overhead of gathering accurate time/material data (often via human expertise) keeps overall cost roughly comparable to a tailor doing quick mental math or using simple templates. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production system reliably performs end-to-end cost estimation for custom garments. Some tools exist for standardized apparel in factories, but custom tailoring cost estimation remains largely manual and relies on tailor expertise and historical records. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | There are no widely deployed products specifically for tailors that estimate garment costs reliably; general-purpose LLMs or spreadsheets can help with arithmetic but lack integrated domain-specific costing tools in production use. |
Confer with customers to determine types of material and garment styles desired.
34CI 30–37 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Confer with customers to determine types of material and garment styles desired.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Tailoring and custom sewing remain craft-oriented, small-business-dominated sectors with low digital adoption rates; adoption of AI for customer consultation is nascent and uncommon in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Custom tailoring is a small-scale, low-digitization trade with minimal AI adoption; the sector lags far behind information/professional services in deploying AI tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants can augment a tailor's consultation by suggesting fabric matches, generating style sketches, tracking customer preferences, and capturing specifications digitally, meaningfully raising efficiency while the human expert remains central to final decisions and relationship-building. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by generating style mood boards, suggesting fabric options, or pre-filling preference forms, aiding the tailor's consultation process without replacing the human interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Customer preference elicitation requires nuanced dialogue, style interpretation, and contextual understanding of aesthetic intent. While AI can suggest fabric types or styles based on keywords, the back-and-forth refinement, fitting constraints, and personalized design consultation remain difficult to automate end-to-end without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves interpersonal consultation, understanding customer preferences, body type, and taste which requires nuanced judgment and physical presence; AI chatbots can gather basic info but can't replace the consultative fitting experience. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Customer preference consultation is not legally restricted, but many customers expect human interaction for custom garment work, and trust in the fit and outcome depends on perceived expertise and personal relationship with the craftsperson. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong customer preference for personal interaction, trust-building, and physical assessment of body/fabric creates organizational and relational friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | A conversational AI system can handle initial consultation at near-zero marginal cost per customer compared to the hourly wage of a tailor or dressmaker, though integration and customization overhead may offset some savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While a chatbot intake form is cheap, the actual value-add of a tailor's consultation (assessing fit, fabric drape, personal style) still requires human expertise, so AI alone doesn't yield a comparable output at lower cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and AI assistants can collect basic preferences in narrow scenarios, but reliable real-world deployment requires handling ambiguous requests, understanding body type constraints, visualizing outcomes, and building rapport—tasks at which current systems perform inconsistently at production scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some virtual styling assistants and chatbots exist for basic preference intake, but no deployed product reliably conducts full custom-garment consultations replacing in-person tailor conversations. |
Record required alterations and instructions on tags, and attach them to garments.
30CI 28–33 · exposure 20 · augmentation 50 · importance 4.0/5 · click for rater detail
Record required alterations and instructions on tags, and attach them to garments.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking are predominantly small, local, low-digitization businesses with minimal AI infrastructure adoption; the task requires fine-grained customization unsuitable for automated workflows. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a low-digitization, small-business-dominated trade with minimal AI adoption for physical workflow steps like tagging. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by auto-generating alteration instruction text or templates from customer notes, reducing writing time and improving clarity, though a tailor must still review and physically attach tags. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Speech-to-text or note-taking apps could help tailors quickly record alteration instructions, improving accuracy and speed of the notation part of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate text for alteration instructions, the task requires physical attachment of tags to garments, which demands robotic manipulation. Current AI systems cannot reliably perform the full end-to-end task of recording, formatting, and physically attaching tags at 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | The physical acts of taking notes on a tag and pinning/attaching it to a garment during a fitting require human presence and manual dexterity; only a small documentation sub-step could be digitized.wrapper |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal licensing requirement exists, but organizational friction is moderate: custom garment shops rely on human expertise to interpret complex alterations, and customers often expect direct human communication about their specific needs. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier, but the task is embedded in a physical, in-person fitting process, creating practical friction against remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The physical attachment component requires specialized robotics or human labor, making total system cost (hardware, software, integration, error correction) likely comparable to or higher than a skilled tailor's wage for this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Any AI assistance would still require a human present for the fitting and physical tagging, so cost savings are marginal relative to the human tailor's time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably records, formats, and physically attaches alteration tags to garments in production environments. This requires both language generation and precise physical manipulation—the latter remains research-stage for general garment handling. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product performs the physical tagging and attachment step; voice-to-text note apps exist but aren't integrated into an end-to-end tailoring workflow. |
Develop, copy, or adapt designs for garments, and design patterns to fit measurements, applying knowledge of garment design, construction, styling, and fabric.
30CI 25–35 · exposure 25 · augmentation 50 · importance 3.9/5 · click for rater detail
Develop, copy, or adapt designs for garments, and design patterns to fit measurements, applying knowledge of garment design, construction, styling, and fabric.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Tailoring and custom sewing remain largely low-digitization, small-firm sectors. While luxury fashion houses use some digital design tools, the sectors dominated by individual tailors and small ateliers have shown minimal adoption of autonomous AI design systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Custom tailoring and small-scale garment work is a low-digitization, craft-based sector where AI tools remain niche and adoption is slow compared to fast-digitizing white-collar sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI design tools can assist tailors by suggesting variations, automating basic pattern scaling from measurements, and visualizing designs on fabric. These features meaningfully accelerate ideation and fit iteration, though the human designer remains essential for creative and construction decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Pattern-drafting software and AI-assisted design tools can speed up initial pattern generation and design iteration, meaningfully aiding tailors while they still perform fitting and construction judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate design ideas and assist with pattern adjustments, the task requires deep integration of garment construction principles, fabric behavior, and custom body measurements. Current AI systems cannot reliably end-to-end design and adapt patterns for fit without substantial human oversight and correction, making 50% time savings at equal quality unlikely. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate design ideas or draft pattern shapes digitally, but fitting patterns precisely to individual body measurements and translating fabric behavior into a wearable garment still requires substantial hands-on tailoring skill and iterative physical fitting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Custom tailoring and dressmaking involve direct client measurement, fitting, and aesthetic judgment that carry liability if incorrect. Industry practice and customer expectations heavily favor human designers, and no regulatory requirement mandates substitution, but client trust and the tangible nature of bespoke work create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing requirement, but custom fit work depends on physical measurement, fabric handling, and client interaction that create practical friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools require high-end software licenses, integration overhead, and skilled operators who must validate outputs. The all-in cost per custom garment design still exceeds the hourly rate of an experienced tailor or dressmaker for typical custom work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software licenses plus the need for skilled human oversight to adapt patterns to real bodies and fabrics make AI-assisted approaches only modestly cheaper, if at all, versus a skilled tailor's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CAD and generative design tools exist for fashion (e.g., CLO 3D, design assistants), but they primarily support visualization and require expert human designers to create functional, wearable patterns. No deployed product reliably performs the full design-to-pattern workflow independently without significant manual intervention. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD pattern-making software and generative design tools exist, but they are narrow-scope aids used by trained pattern makers rather than end-to-end autonomous systems producing fitted garments reliably. |
Position patterns of garment parts on fabric, and cut fabric along outlines, using scissors.
25CI 15–35 · exposure 13 · augmentation 25 · importance 4.0/5 · click for rater detail
Position patterns of garment parts on fabric, and cut fabric along outlines, using scissors.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is slow and concentrated in high-volume industrial garment production (factories, not custom tailoring). Small tailoring businesses—where this task statement applies—show minimal AI/robotic adoption; most remain manual or use decades-old semi-automated cutting tables. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring and dressmaking are low-digitization, small-shop, highly manual trades with minimal AI or robotics adoption for this specific physical task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted pattern layout (digital markers, nesting optimization) offers some productivity gain, but current systems offer limited real-time assistance during manual cutting itself; most augmentation remains at the design/planning stage rather than the execution stage. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital pattern-making and layout software can help optimize fabric use and pattern placement digitally, offering some assistance, but the physical cutting itself remains unaided by AI. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cutting fabric along pattern outlines requires precise spatial reasoning, handling of materials, and real-time adaptation to fabric variability. While AI vision systems can identify patterns, current robotic systems struggle with the dexterity, force control, and material-specific adjustments needed to cut consistently at scale; this remains largely manual or semi-automated with heavy human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterous handling of flexible fabric, precise pattern alignment, and cutting with scissors—robotics and AI cannot yet perform this reliably outside narrow industrial contexts with rigid materials. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal licensing requirement exists for fabric cutting itself, but material handling and quality verification remain human-dependent due to liability and fit requirements; organizational friction is low, but technical barriers to reliable automation are substantial. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the physical dexterity, material variability, and need for tactile judgment in handling fabric create strong practical barriers to automation, independent of regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic fabric-cutting systems are capital-intensive (six figures to millions for industrial cutters) with high integration costs; amortized per-task cost typically exceeds the loaded wage of a skilled cutter for custom work, especially in small operations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Automated cutting systems (e.g., CNC fabric cutters) are expensive capital investments impractical for per-garment custom work, making them costlier than a human tailor for one-off or small-batch tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Experimental fabric-cutting robots exist in research and limited industrial settings (e.g., automotive textiles), but reliable, general-purpose systems for custom garment cutting are not deployed at scale in production tailoring shops. Error rates and setup costs remain prohibitive for most small-to-medium tailoring operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No consumer or widely deployed product performs custom pattern placement and cutting of fabric with scissors; industrial fabric cutters exist for mass production but not for the flexible, judgment-based custom tailoring described here. |
Examine tags on garments to determine alterations that are needed.
23CI 14–33 · exposure 20 · augmentation 38 · importance 3.9/5 · click for rater detail
Examine tags on garments to determine alterations that are needed.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking remain predominantly small, physical-service businesses with low digital infrastructure. Adoption of automation in this sector is minimal, and many customers value the personal consultation inherent in the alteration process. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Tailoring and custom sewing is a low-digitization, small-scale, physical craft sector with minimal AI agent adoption in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted tag reading and garment classification could help a tailor organize and log garment information faster, speeding up the documentation phase. However, the core task of determining what alterations are needed is inherently human judgment, so augmentation is modest. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially help interpret written alteration notes or digitize measurements, but it offers little assistance for the core physical tag examination and fitting judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While computer vision can identify garment tags and read text, determining what alterations are needed requires understanding fit, style intent, and customer preferences—judgment that current AI systems cannot reliably automate end-to-end. Tag examination is only the first step; assessment of fit problems and alteration strategy remains fundamentally human. |
| Task automatability | claude-sonnet-5 | 2/5 | Reading pin marks or tags to determine alterations requires physical inspection combined with fine visual and contextual judgment that current AI cannot reliably perform end-to-end without human handling of the garment.ance.aspx.rrationale.txt truncated.aspx.ok anyway.ok.txt.ok.txt.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt.ok.txt |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Alterations require aesthetic and fit judgment that customers expect from a skilled professional; there is strong organizational and customer preference for human expertise in this domain. The liability for incorrect alteration decisions also creates practical barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific step, but it requires physical presence and hands-on garment handling, creating practical friction to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Computer vision inference is cheap, but the overhead of integrating it into a tailor's workflow, handling exceptions, and requiring human review for actual decisions makes the per-task cost comparable to or exceeding a brief human inspection of a garment's tags. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI systems cannot currently perform this physical inspection task, so there is no viable AI cost basis to compare against human labor for this specific action. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision systems can read text on tags and identify garment type with reasonable accuracy, but no deployed product reliably determines what alterations a specific garment actually needs based on tag inspection alone. Production systems exist for OCR but not for the interpretive judgment this task requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product examines physical alteration tags on garments and determines needed sewing alterations; this remains a manual, physical-inspection task performed by tailors. |
Press garments, using hand irons or pressing machines.
23CI 10–35 · exposure 13 · augmentation 13 · importance 4.1/5 · click for rater detail
Press garments, using hand irons or pressing machines.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Garment pressing occurs in small custom shops, dry cleaners, and tailoring operations that are typically low-digitization, small-scale businesses with limited capital for automation. Adoption of robotic pressing remains very limited outside industrial textile facilities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | The apparel/tailoring sector is low-digitization and physically manual, showing minimal AI/robotics adoption for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance to human pressers; modern pressing machines are primarily physical tools with limited decision support. There is little scope for AI to augment human judgment in this fundamentally manual, sensorimotor task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to the physical act of pressing garments with irons or pressing machines. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Pressing garments requires handling delicate, variable fabrics with precision and situational awareness (detecting fabric type, heat sensitivity, moisture). While industrial pressing machines exist, they require setup, fabric placement, and quality judgment that current AI systems cannot reliably perform end-to-end without significant human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | Pressing garments is a physical manipulation task requiring fine motor control, fabric handling, and adaptive judgment about heat/pressure per fabric type; no off-the-shelf AI system performs this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Garment pressing involves direct contact with customer goods and quality standards that create some organizational friction and expectation for human oversight, but no strict legal licensing requirement exists. Adoption barriers are moderate rather than hard. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human presser, but physical dexterity and fabric-specific judgment create practical friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic garment pressing systems are capital-intensive and still require human operators for setup, quality control, and exception handling. The all-in cost (hardware, maintenance, oversight) typically exceeds or approaches the loaded wage of a skilled presser, making substitution economically marginal. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Human labor with basic pressing equipment remains far cheaper than any hypothetical AI-robotic solution, which would require expensive specialized hardware and integration with no current market offering. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial pressing machines are deployed but are semi-automated and operator-dependent; they do not autonomously handle the full task of receiving, positioning, pressing, and checking garment quality. No current robotic or AI system reliably performs garment pressing at production scale without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI/robotic products performing garment pressing at commercial scale; industrial pressing automation that exists is fixed mechanical equipment, not AI-driven perception/manipulation. |
Assemble garment parts and join parts with basting stitches, using needles and thread or sewing machines.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.2/5 · click for rater detail
Assemble garment parts and join parts with basting stitches, using needles and thread or sewing machines.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking remain largely small-firm, artisanal sectors with low overall automation rates. Adoption of robotic garment assembly is negligible; most work continues by hand or traditional sewing machines in local shops. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Tailoring and custom sewing is a low-digitization, small-firm, physically-oriented trade with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Sewing machines themselves are long-standing augmentation tools, but AI-driven assistance for garment assembly (e.g., pattern guidance, stitching feedback) is minimal today. Some CAD and cutting tools exist, but real-time stitching assistance remains underdeveloped. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers limited assistance here—perhaps in pattern generation or measurement—but the physical basting and assembly itself gains little from current AI tools. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI robotics and vision systems struggle with the fine motor control, fabric handling, and real-time adaptation required to assemble garment parts and execute basting stitches reliably. While some prototype systems exist, they cannot match human dexterity or consistency across varied fabric types, making a 50% time-saving threshold unattainable today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically assembling and basting fabric pieces requires fine manipulation, material handling, and adaptive dexterity that current robotics and AI cannot perform reliably outside narrow industrial contexts.rough automation exists only for high-volume standardized sewing, not custom garment work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there is no strict licensing requirement for automation itself, customer preference for handcrafted or custom-fitted garments, the need for real-time quality inspection, and the artisanal nature of tailoring create moderate organizational and market friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but the physical craft skill, material variability, and customer expectation of hand-tailored quality create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of any garment assembly are extremely expensive to purchase, program, and maintain, far exceeding the loaded wage of a skilled tailor or seamstress for equivalent output. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic sewing systems capable of handling varied fabrics and custom garments require expensive specialized hardware, making them far costlier than a human tailor for low-volume custom work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs garment assembly and basting at production scale. Robotic sewing remains largely in research and experimental settings with narrow, controlled fabric inputs; no mature system exists in real tailoring or dressmaking shops. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or small-business product performs custom garment basting/assembly autonomously; sewing robots remain research/industrial-niche (e.g., SoftWear Automation) and not applicable to bespoke tailoring. |
Sew garments, using needles and thread or sewing machines.
19CI 5–33 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail
Sew garments, using needles and thread or sewing machines.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The tailoring and dressmaking sectors remain largely small-scale, craft-based, and low-digitization. Adoption of AI-driven automation in these sectors is minimal; production remains concentrated in human workers and traditional industrial sewing lines. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Garment sewing, especially custom tailoring, is a low-digitization, physical craft sector with minimal AI/robotic adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with pattern design and layout optimization, but offers limited real-time help during the actual sewing process itself. The physical, tactile nature of garment construction leaves little room for meaningful AI augmentation of the core sewing task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with pattern design, measurements, or CAD-based cutting but offers little direct assistance to the physical act of sewing itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI robotics cannot reliably handle the fine motor control, fabric manipulation, and real-time quality adjustment required for full garment sewing. Partial automation of repetitive stitching is possible with specialized industrial machines, but end-to-end garment creation from raw fabric remains beyond practical automation at 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical sewing of custom garments requires fine motor manipulation of fabric, needles, and thread that current AI systems and robotics cannot perform reliably; no off-the-shelf system does this end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Custom tailoring and dressmaking involve close human-contact requirements, fit adjustments, and quality assurance that traditionally demand human judgment and presence. Regulatory and customer preference barriers favor human craftspeople for bespoke work. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but physical dexterity, material handling, and quality/fit expectations for custom garments create strong practical barriers to automation beyond regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic sewing systems, where they exist, require significant capital investment, specialized setup, and ongoing maintenance that exceeds the labor cost of skilled sewers in most markets. Integration and oversight costs remain high relative to human wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized sewing robots (where they exist experimentally) are far more expensive to develop and deploy than paying a human tailor for custom work, especially at small scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While industrial sewing machines exist, they are rigid, task-specific tools that cannot autonomously adapt to fabric variation, pattern changes, or quality inspection. No deployed AI-driven robotic system reliably performs complete garment sewing in production environments today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that autonomously sew custom garments in production; robotic sewing remains largely research-stage due to fabric's deformability and variability. |
Measure customers, using tape measures, and record measurements.
19CI 5–33 · exposure 13 · augmentation 38 · importance 4.3/5 · click for rater detail
Measure customers, using tape measures, and record measurements.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring is a traditional, low-digitization craft sector with limited automation adoption. Small shops and individual artisans dominate, and the bespoke nature of the work creates organizational resistance to AI-driven displacement. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a small-scale, low-digitization trade with minimal enterprise AI adoption; most shops still rely on manual measuring. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could offer minor assistance such as auto-recording measurements verbally or storing them digitally, but the core measurement task—holding the tape, positioning it, reading values—remains entirely human-dependent. Augmentation potential is minimal. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital measurement tools and apps can help record and store measurements more accurately and consistently, aiding the tailor without replacing the physical measuring process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Taking accurate body measurements requires physical presence, tactile sensing, and precise positioning of measuring tape against a customer's body—capabilities that current AI systems fundamentally lack. Automation of the measurement act itself remains entirely infeasible with deployed technology. |
| Task automatability | claude-sonnet-5 | 2/5 | Basic body measurements can be captured via smartphone-based 3D scanning apps, but precise custom-fit measurement for tailoring still generally requires manual, hands-on assessment of fit and fabric behavior.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: customers expect direct human contact for fitting and measurement accuracy; measurements must be reliable for garment fit; and liability for incorrect measurements rests with the tailor. Trust and physical presence are near-requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but customer comfort with physical touch/human judgment during fitting creates some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any robotic system capable of taking accurate body measurements would be extremely expensive to acquire, maintain, and deploy compared to a human tailor's labor cost for this straightforward manual task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | 3D scanning hardware/software has upfront and integration costs that may not undercut a tailor's few minutes of manual measuring, especially at small-shop scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously measure human bodies or operate measuring instruments. This task requires embodied robotics or human presence, which is not available in production use for tailoring contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some body-scanning apps and smart tape measures exist commercially, but they are not widely deployed in custom tailoring shops and accuracy for fine tailoring purposes remains inconsistent. |
Repair or replace defective garment parts, such as pockets, zippers, snaps, buttons, and linings.
17CI 10–24 · exposure 8 · augmentation 25 · importance 4.1/5 · click for rater detail
Repair or replace defective garment parts, such as pockets, zippers, snaps, buttons, and linings.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking remain low-digitization, small-firm dominated sectors with minimal industrial automation adoption. No measurable production deployment of AI or robotic repair systems exists in this occupational space. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | The tailoring and garment repair sector is a low-digitization, physical craft trade with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by identifying defect types or suggesting repair approaches via computer vision, but the hands-on, craft-oriented nature of the work limits assistance value. Current tools offer minimal augmentation relative to a tailor's expertise and visual assessment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with pattern matching, sourcing replacement parts, or providing repair instructions, but offers little direct help with the hands-on repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can identify defective parts in images, the physical manipulation required to remove and replace zippers, buttons, or linings demands dexterous robotics that remains unreliable in real-world garment variation. Current systems cannot achieve the 50% time-saving threshold end-to-end without human intervention on most defect types. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring fine motor skills, fabric handling, and judgment about garment construction; no off-the-shelf AI system can perform hands-on repair of pockets, zippers, or linings today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict legal licensing bars automation in most jurisdictions, custom garment repair carries high error-cost asymmetry (a botched repair damages valuable clothing) and strong customer preference for human craftsmanship; these create practical friction but do not constitute hard regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing is required, but the physical dexterity, variability of garment defects, and need for tactile handling create strong practical (though not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of garment repair (including vision, manipulation, and quality control) carry capital costs and integration overhead far exceeding the loaded wage of a skilled tailor or seamstress, making full-system automation economically unviable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI-driven robotic system exists for this task at any comparable cost to a human tailor, so AI is effectively far more expensive or simply unavailable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this task autonomously today. Robotic sewing and garment manipulation remain in research and early prototype stages; no production systems at scale handle the variety of garment geometries, materials, and defect types this task requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that autonomously repair garments; this remains firmly a manual craft task performed by humans with sewing machines and hand tools. |
Remove stitches from garments to be altered, using rippers or razor blades.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.3/5 · click for rater detail
Remove stitches from garments to be altered, using rippers or razor blades.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking are craft trades with low overall digitization, small firm dominance, and minimal AI adoption; this specific manual task sees no meaningful production automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Tailoring is a small-scale, low-digitization physical craft sector showing minimal AI or robotics adoption for manual sewing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI and automation tools offer no practical assistance to a human removing stitches; the task is too tactile and fine-motor-dependent for augmentation to add value. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of removing stitches; this is a manual dexterity task outside current AI's scope. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Removing stitches requires dexterous fine motor control, spatial reasoning about fabric structure, and sensitivity to delicate material—capabilities that current AI and robotics cannot reliably perform. End-to-end automation with quality preservation is not demonstrated at any scale today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires fine physical manipulation and dexterity to remove stitches without damaging fabric, a task current AI systems (software-based) cannot perform, and robotics cannot yet do reliably outside labs. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal licensing is required, but strong organizational and craft traditions, customer expectations for human quality control, and the physical/tactile nature of the work create moderate friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but the physical dexterity and variability of garments/fabrics creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of fine manipulation and fabric handling would be extremely expensive to develop, deploy, and maintain, far exceeding the cost of a skilled tailor's labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution to compare costs against; human labor via simple hand tools remains the only practical and cheaper option today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI system reliably performs stitch removal on varied garments in production environments. This task remains fundamentally manual and craft-based; research prototypes do not meet production reliability thresholds. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs seam-ripping on garments in production; this remains a manual craft task with no commercial automation. |
Put in padding and shaping materials.
13CI 10–15 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Put in padding and shaping materials.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Apparel manufacturing and custom tailoring remain labor-intensive and slow to digitize; small tailoring shops and bespoke dressmaking are not adopting automated solutions for padding insertion. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring and small-scale garment work are low-digitization, physical craft sectors with minimal AI/robotics adoption for hands-on construction tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a tailor performing this task; the work requires hands-on material manipulation where AI cannot provide guidance or augmentation today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with pattern design, measurements, or material selection guidance, but offers little direct assistance for the physical act of inserting padding and shaping materials. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Inserting padding and shaping materials requires precise hand placement, understanding of fabric properties, and three-dimensional spatial reasoning within garments. Current AI systems cannot perform fine motor manipulation tasks or operate sewing equipment end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Inserting padding and shaping materials (interfacing, shoulder pads, boning) requires fine tactile manipulation, fabric handling, and physical dexterity that current AI and robotics cannot perform end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no hard legal barriers to automation, the craft nature of the work and customer expectations for hand-finished garments create organizational and market friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human do this, but physical dexterity requirements and lack of technology create strong practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of textile manipulation are extremely expensive to develop, integrate, and maintain, far exceeding the loaded wage of a skilled tailor for this specific task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so any hypothetical automation would require expensive bespoke robotics far costlier than a human tailor's labor for this operation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs garment padding and shaping insertion reliably in production. This remains a manual craft task requiring physical dexterity that even advanced robotics have not solved at commercial scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical garment construction step; robotic sewing/fabric manipulation remains research-stage due to fabric's deformable, unpredictable nature. |
Measure parts, such as sleeves or pant legs, and mark or pin-fold alteration lines.
12CI 5–19 · exposure 8 · augmentation 25 · importance 4.3/5 · click for rater detail
Measure parts, such as sleeves or pant legs, and mark or pin-fold alteration lines.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking remain artisanal, small-business-dominated sectors with low digitization. Adoption of AI in this domain is minimal; most shops are independently operated and lack the infrastructure for advanced automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Tailoring is a small-shop, highly manual, low-digitization trade with essentially no AI/robotics adoption for physical alteration work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by suggesting standard alteration measurements or highlighting reference points in customer photos, but the core task of hands-on measurement and physical marking offers limited augmentation value. The human skill in translating fit to precise alterations remains central and difficult to enhance via AI. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven body scanning or measurement apps can assist with digital measurements, but the core physical marking and pin-fitting is still entirely manual with minimal AI augmentation currently. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Measuring and marking alteration lines requires precise physical manipulation in 3D space with variable garment geometry. While image analysis could identify some reference points, the end-to-end task of physically measuring, marking, and pin-folding—especially handling fabric texture and drape—remains beyond practical automation today. Partial digitization of measurement recording is possible but doesn't meet the 50% time-saving threshold for the full task. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of fabric on a body or dress form, precise tactile measurement, and manual pinning/marking, none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Custom alteration work typically requires hands-on customer interaction and fitting—garments must be tried on and adjusted iteratively. Customer preference for human judgment and direct consultation creates organizational friction, and liability concerns around fit and quality errors add friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but the task demands fine motor dexterity, fitting on human bodies, and customer interaction that create strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment cost (specialized robotics, computer vision, garment handling systems) to automate this task would far exceed the loaded wage of a skilled tailor or dressmaker. The capital and integration costs make substitution economically unfeasible today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system performing this physical task, so any hypothetical automation would require expensive custom robotics far costlier than a human tailor's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably performs the physical measurement and pin-folding components of this task. Vision systems can detect garment landmarks in images, but robotic systems capable of accurate soft-goods manipulation, measurement, and marking at production scale do not exist in commercial deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical garment measuring and pin-marking; this remains purely a manual, hands-on tailoring task with no commercial robotic solution in production. |
Fit, alter, repair, and make made-to-measure clothing, according to customers' and clothing manufacturers' specifications and fit, and applying principles of garment design, construction, and styling.
12CI 5–19 · exposure 8 · augmentation 38 · importance 4.1/5 · click for rater detail
Fit, alter, repair, and make made-to-measure clothing, according to customers' and clothing manufacturers' specifications and fit, and applying principles of garment design, construction, and styling.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking remain concentrated in small, localized firms with low digitization; adoption of advanced automation is minimal. The sector is not experiencing production-level AI adoption comparable to information services or finance, keeping velocity laggard. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | The tailoring and custom garment sector is a low-digitization, small-business-dominated, physically-intensive trade with minimal AI/robotics adoption in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with pattern optimization, measurement recording, and design suggestions, but current systems offer limited help with the core embodied skills of fitting, draping, sewing, and real-time adjustment. Assistance is narrow and partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with design visualization, pattern generation, sizing calculations, and customer specification management, but it does not touch the core physical fitting and sewing work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft patterns and suggest alterations from measurements, the task requires physical fitting work, real-time adjustment based on garment behavior, and artistic judgment about fit and styling that current systems cannot fully perform end-to-end. AI lacks the embodied skill to actually sew, fit on bodies, or achieve the ≥50% time-saving threshold for the complete task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a highly physical, manual task requiring hands-on manipulation of fabric, precise measurement of individual bodies, and fine motor skills that current AI systems cannot perform end-to-end without robotic embodiment far beyond today's capabilities. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Made-to-measure and alteration work require direct human contact with customers for fitting, measurements, and iterative refinement; customer preference for human craftsmanship is strong; and liability concerns around fit errors and garment damage create friction. However, no single license legally mandates a human perform the task, preventing a rating of 5. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists for tailoring, but the task requires physical presence, direct body measurement, and tactile handling of materials, creating strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even accounting for AI-assisted pattern drafting or measurement analysis, the labor cost of a skilled tailor remains substantially lower than the combined cost of AI systems, hardware, integration, human oversight of fit decisions, and the physical automation infrastructure (robotics, etc.) that would be needed. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical labor, so any comparison would require expensive robotics/human oversight, making AI far more costly than a human tailor for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full cycle of fitting, altering, repairing, and constructing custom garments. Measurement and design aids exist as research prototypes, but production systems that handle the physical execution, quality control, and customer-specific adjustments required here are not deployable today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical garment fitting, alteration, or sewing; robotic sewing remains research-stage and limited to narrow, controlled tasks, not full custom garment construction. |
Let out or take in seams in suits and other garments to improve fit.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail
Let out or take in seams in suits and other garments to improve fit.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring is a small, dispersed, low-digitization sector with few large firms and minimal technology adoption. Most tailors still work in small independent shops with manual processes, showing no meaningful velocity toward AI or automation tools. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a small-scale, low-digitization trade with minimal AI/robotics adoption and no evidence of production deployment for physical alteration work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to a tailor performing seam adjustments. There are no deployed tools that help with fit measurement, seam calculation, or physical execution—the core components of this task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design visualization, pattern generation, or measurement estimation via apps, but offers little help with the actual manual seam alteration process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Letting out or taking in seams requires physical manipulation of fabric, precise measurement relative to a customer's body, and real-time judgment about fit—tasks that current robotics and AI cannot perform end-to-end reliably. No deployed system can autonomously handle fabric, measure fit, and execute seam adjustments at quality parity with a skilled tailor. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of fabric, measuring against a body, cutting, pinning, and precision sewing - fundamentally a manual dexterity task that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: this is a hands-on craft requiring direct physical presence with the customer, individualized judgment on fit, and human accountability for quality. Customer preference for human skill and craftsmanship, plus the need for try-on and iterative adjustment, create substantial organizational and social friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but the task demands physical dexterity, individualized fit assessment, and customer trust in tailoring skill, creating strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The labor cost of a skilled tailor (typically $15–30+ per hour in developed markets) is far lower than the combined cost of robotic systems, vision setup, fabric handling infrastructure, and human oversight required for even a narrow automation attempt. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system to compare cost against; a human tailor remains the only functional option, making AI substitution infinitely more expensive or simply unavailable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production system exists that can autonomously let out or take in garment seams. This task demands dexterous manipulation, spatial reasoning about fabric geometry, and integration with human try-on feedback—all beyond deployed robotic or AI capabilities today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs garment alteration fitting adjustments in production; this remains firmly in the human-hands domain with no commercial automation. |
Fit and study garments on customers to determine required alterations.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Fit and study garments on customers to determine required alterations.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring is a traditional, low-digitization sector with small independent shops and artisanal practices. Adoption of AI-driven automation in this space has been negligible, and the sector remains highly resistant to technology substitution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a small-scale, low-digitization, highly manual trade with minimal AI adoption pressure or infrastructure. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI body-scanning or visualization tools might assist a tailor by providing reference measurements or virtual mockups, but the core task—physical fitting and judgment on a customer's body—remains dependent on human expertise and presence, limiting augmentation value. |
| Augmentation potential | claude-sonnet-5 | 2/5 | 3D body scanning and measurement apps can assist in gathering initial data, but they offer limited help with the nuanced, hands-on judgment needed for actual fitting and alteration decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human presence and tactile interaction—draping, pinning, and physically studying how fabric sits on a specific body. Current AI cannot perform the hands-on fitting, measurement-taking, and real-time adjustment that customers expect and that determine alteration quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Fitting garments requires physical manipulation, tactile assessment of fabric drape, and reading a customer's body in real time—none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Customer expectations and trust strongly favor human tailors for fitting; there is organizational and cultural lock-in around personalized service. Liability concerns around poor fits also create friction, and the human-contact element is nearly irreplaceable for customer satisfaction and comfort. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but strong physical/human-contact requirements and customer trust in hands-on service create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is labor-intensive and highly skilled; any current AI assistance (e.g., body scanning) adds cost without eliminating the need for a tailor's time and judgment. Automation would not achieve cost parity with the human labor it would need to replace. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system to compare cost against for physical fitting; any robotic or sensor-based solution would be far more costly than a tailor's labor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial system can independently fit and study garments on live customers. While body-scanning and virtual try-on technologies exist in research and limited e-commerce contexts, they do not replace in-person fitting by skilled tailors, and the task requires human presence. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical garment fitting on customers; body-scanning and virtual try-on tools exist but are not substitutes for hands-on fitting and alteration marking. |
Make garment style changes, such as tapering pant legs, narrowing lapels, and adding or removing padding.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Make garment style changes, such as tapering pant legs, narrowing lapels, and adding or removing padding.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Garment alteration shops are typically small, independent, or regional businesses with low digitization and capital investment. Adoption of industrial automation in this segment has historically been minimal, and custom fitting work resists standardization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a small-scale, low-digitization trade with minimal AI adoption; robotic sewing and fitting automation remain largely experimental in this sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with measurement logging, pattern recommendations from photos, or inventory tracking, but offers minimal productivity gain for the core manual skill of physically altering garments to fit individual bodies and aesthetic preferences. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with pattern design, measurement calculations, or visualizing alterations via apps, but it offers little help with the actual physical cutting and sewing work central to this task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of garments in three dimensions, precise measurement adapted to individual body geometry, and real-time decision-making about fit and aesthetics. Current AI cannot operate sewing machines or handle fabric; the task is fundamentally embodied and manual. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical manipulation task requiring cutting, pinning, and sewing fabric with fine motor skill and fitting judgment—current AI systems have no capability to perform physical alterations end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Custom garment alteration is inherently human-contact work requiring fitting sessions, visual inspection, and negotiation of aesthetic preferences with the customer. Liability for ill-fitting or damaged garments remains with the provider and favors human craftsmanship. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but the task demands physical dexterity, customer fitting interaction, and tactile judgment that create strong practical (though not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and operational cost of any robotic system capable of tapering legs, narrowing lapels, or adjusting padding would vastly exceed the hourly wage of a skilled tailor or dressmaker, even at scale. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical task, so any AI cost is irrelevant compared to the human tailor's labor cost—AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product can autonomously perform garment alterations. The task requires robotic dexterity, spatial reasoning about fit, and material handling that remains in the research phase, not production deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical garment alteration; sewing robots remain research-stage and cannot handle the variability of custom fitting work. |
Take up or let down hems to shorten or lengthen garment parts, such as sleeves.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Take up or let down hems to shorten or lengthen garment parts, such as sleeves.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tailoring and dressmaking remain craft-oriented, small-firm, low-digitization sectors with minimal AI adoption; the task involves physical, bespoke work resistant to industrial automation patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Garment alteration is a small-scale, low-digitization, physical craft sector with essentially no AI/robotic adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with measuring and documenting alterations via computer vision, but the core creative and physical work of hemming offers limited scope for meaningful augmentation of the tailor's labor. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations, pattern suggestions, or design visualization, but offers minimal help with the actual physical hemming work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Hemming requires physical manipulation of fabric, precise measurement, sewing skill, and judgment about fit on a human body. Current AI systems cannot operate sewing machines or perform the fine motor control needed to alter garments; this task remains firmly in the domain of human craftspeople. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine-motor physical task requiring measuring, pinning, cutting, and sewing fabric by hand or machine; no current AI system can physically manipulate garments to alter hems.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Custom tailoring is inherently hands-on work performed directly on client garments, and customers strongly prefer human expertise and accountability. The task's intimate nature (fitting on bodies, aesthetic judgment) creates both organizational and market friction against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but physical dexterity, fabric variability, and customer fit requirements create strong practical barriers to automation beyond regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automated hemming would require custom robotics setup (design, capital equipment, integration) that far exceeds the cost of a skilled tailor performing the work manually, making AI substantially more expensive in practice. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical task, so any AI-based approach would require expensive robotics far exceeding the cost of a human tailor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs hemming end-to-end. While computer vision could measure sleeve length, the actual sewing and physical alteration require robotic systems not in commercial production for custom tailoring at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical hem alteration; robotic sewing/garment manipulation remains research-stage and not commercially deployed for bespoke alterations. |
Maintain garment drape and proportions as alterations are performed.
5CI 0–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Maintain garment drape and proportions as alterations are performed.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Custom tailoring and dressmaking are low-digitization sectors with small firms and strong craft traditions; adoption of AI in alteration work remains negligible because the physical and aesthetic judgment required has no substitute. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Custom tailoring is a low-digitization, small-scale craft trade with minimal AI tool integration into the physical alteration process itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by analyzing before-and-after images or suggesting proportional changes, but the core task—feeling fabric drape and adjusting fit in real-time—relies on human sensorimotor skill that AI cannot meaningfully augment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with measurements, pattern suggestions, or visualization of fit via 3D body scanning, but it offers little direct help with the hands-on task of maintaining drape and proportion during physical alteration. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Maintaining garment drape and proportions during alterations requires fine spatial judgment, tactile feedback, and aesthetic assessment that current AI systems cannot perform end-to-end. No AI system can physically manipulate fabric or make real-time adjustments based on how a garment hangs on a body. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of fabric, spatial-tactile judgment, and real-time fitting on a body or form—no current AI system can physically alter garments or assess drape through touch and visual inspection during hands-on work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task inherently requires a licensed or skilled human to perform: clothing alteration depends on hands-on craftsmanship, immediate visual and tactile feedback, and accountability for fit on the customer. Legal and professional standards require human expertise and sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists, but the task requires physical dexterity, judgment on fabric behavior, and customer fitting interaction that inherently resist automation without robotic embodiment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation and embodied judgment that AI cannot provide, so AI cost per equivalent output is undefined or infinite—a human tailor remains necessary. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI cost comparison is moot—human labor is the only option, making AI effectively infinitely more 'expensive' since it cannot perform the task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs this task autonomously. While computer vision can analyze images of garments, no production system can assess drape quality, adjust fabric in real-time, or maintain proportions during actual alterations without human intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical garment alteration; this remains entirely a manual craft skill requiring hand-eye-tactile coordination that robotics/AI cannot replicate at consumer or production scale 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.