Patternmakers, Wood
51-7032.00Median wage $49,630/yr220 employed (US)Rank #431 of 923 scored · top 47% by substitution
Plan, lay out, and construct wooden unit or sectional patterns used in forming sand molds for castings.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution29
Exposure22
Augmentation35
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
20 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
10%
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%25
panel mean rating 2.0/5 → substitution pressure 25/100
Technical feasibility todayw 20%16
panel mean rating 1.6/5 → substitution pressure 16/100
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%9
panel mean rating 1.4/5 → substitution pressure 9/100
Task breakdown (20 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.
Compute dimensions, areas, volumes, and weights.
87CI 75–100 · exposure 87 · augmentation 88 · importance 3.9/5 · click for rater detail
Compute dimensions, areas, volumes, and weights.
87| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Woodworking and manufacturing have rapidly adopted digital design and CAD tools; computation of geometric properties is standard embedded functionality in production CAD environments across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Wood patternmaking is a small, traditional manufacturing trade with lower digitization rates than white-collar sectors, so overall AI/software adoption for this specific occupation is slower even though the calculation tools exist. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI-assisted design tools (generative design, parametric modeling) help patternmakers iterate faster on dimensions and properties, significantly raising productivity while keeping the designer in control of aesthetic and functional decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | CAD and calculation software substantially speed up and improve accuracy of dimensional computations while the patternmaker still designs and fabricates the actual pattern. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Computing dimensions, areas, volumes, and weights from designs or physical specifications is a fully algorithmic task that modern CAD software, spreadsheet applications, and AI can perform end-to-end with 50%+ time savings compared to manual calculation and transcription. |
| Task automatability | claude-sonnet-5 | 4/5 | Computing dimensions, areas, volumes, and weights from specifications is a well-defined mathematical task that CAD software, spreadsheets, and AI tools handle reliably with minimal setup, though it still requires accurate input of specs and physical measurements/checks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | No legal, licensing, or liability barriers prevent automation of pure mathematical computation; patternmakers adopt CAD and calculation tools routinely without regulatory impediment. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, regulatory, or liability barrier to using calculation software for this sub-task; it's already standard practice in the trade. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Software-based computation costs pennies per task execution, orders of magnitude cheaper than paying skilled labor for manual calculation and verification of geometric properties. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Software-based calculation is extremely cheap compared to skilled labor time spent manually computing dimensions and volumes, though integration with a full patternmaking workflow adds some overhead. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature, production-grade software (CAD systems like AutoCAD, FreeCAD; mathematical engines; spreadsheet tools) reliably perform these calculations at scale in design and manufacturing workflows worldwide. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | CAD/CAM software and calculation tools already perform these computations reliably in production woodworking and patternmaking workflows, though some manual verification against physical patterns is still standard practice. |
Inventory equipment and supplies, ordering parts and tools as necessary.
74CI 70–77 · exposure 75 · augmentation 75 · importance 3.4/5 · click for rater detail
Inventory equipment and supplies, ordering parts and tools as necessary.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Small woodworking shops and patternmakers have moderate digitization; larger facilities and contract manufacturers adopt inventory automation readily, but fragmented, small-scale operations adopt slowly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Wood patternmaking is a niche, small-scale manufacturing trade with generally low digitization and slow tech adoption compared to information/finance sectors, even though the underlying task type is common in other industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Automated stock alerts and reorder suggestions significantly raise human efficiency by eliminating manual counting and supplier-hunting time, leaving the craftsperson to focus on quality assessment and vendor relationship decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted inventory software can significantly boost efficiency by tracking stock levels, predicting reorder needs, and automating purchase orders while the patternmaker retains oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Inventory management can be largely automated through barcode scanning, RFID systems, and stock-level monitoring AI; ordering logic is straightforward rules-based work. The task requires some human judgment on wood-specific supply quality and supplier relationships, preventing a full 5, but automation can achieve substantial time savings. |
| Task automatability | claude-sonnet-5 | 4/5 | Inventory tracking and reordering of parts/tools is a structured, rule-based task that off-the-shelf inventory management software and AI-driven procurement systems can largely handle end-to-end with significant time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human inventory management; the main friction is organizational inertia and preference to maintain direct supplier relationships, but these are soft barriers that adoption readily overcomes. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory barriers prevent automating inventory and procurement; it's a purely administrative/logistical task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated inventory systems cost relatively little per transaction compared to hourly labor for manual stock checks and ordering; a modest software subscription ($50–200/month) handles what would occupy hours of manual work weekly. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated inventory systems and reorder software are relatively cheap to run compared to a skilled patternmaker's time spent on manual counting and ordering, though small shops may have upfront integration costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Inventory management software with automated reordering is mature and widely deployed in manufacturing and skilled trades; systems like Shopify, Oracle, and specialized workshop inventory tools perform this reliably at scale. Integration requires manual initial setup but operates dependably thereafter. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature inventory management and procurement automation products (e.g., ERP systems with reorder triggers, barcode/RFID scanning, automated purchasing) are widely deployed in manufacturing and shop settings today. |
Estimate costs for patternmaking jobs.
59CI 33–85 · exposure 58 · augmentation 63 · importance 4.1/5 · click for rater detail
Estimate costs for patternmaking jobs.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing and skilled trades sectors show uneven AI adoption; while estimation tools are available, uptake remains moderate relative to information-sector automation. Many patternmaking shops are small and conservative, slowing deployment velocity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a niche, low-digitization craft trade with minimal AI tool adoption or public evidence of AI-driven cost estimation in this specific field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI cost-estimation assistants can substantially boost a patternmaker's productivity by automating data aggregation, calculations, and draft estimates, allowing the human to focus on design nuance and client communication while staying in control. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by providing quick reference estimates, spreadsheet automation, or historical job comparisons, helping speed up the estimating process while the patternmaker still finalizes based on expertise. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Cost estimation for patternmaking jobs involves data lookup, calculation, and template application—tasks that AI systems can perform end-to-end with material time savings. Large language models and spreadsheet agents can gather material costs, labor rates, overhead factors, and generate detailed estimates faster than manual processes. |
| Task automatability | claude-sonnet-5 | 2/5 | Cost estimation requires judgment about material, labor, complexity, and shop-specific pricing that AI could partially support but not fully replace without significant domain-specific data integration.ID typically low-volume, bespoke work resists full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensure, regulatory requirement, or legal mandate requires a human to sign off on cost estimates. The main friction is organizational preference for human review and client expectations, not hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement mandates a human for cost estimation, but customer trust and craft-specific tacit knowledge create moderate organizational friction against pure AI adoption. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference and integration cost is negligible compared to the wage of a skilled patternmaker (typically $45k–$65k annually), making automation cost-effective by an order of magnitude even after accounting for setup and oversight. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools could cheaply generate rough estimates, but integration and validation against a skilled patternmaker's tacit knowledge of material behavior and labor time adds overhead, keeping costs comparable to human effort for accurate results. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | AI-powered cost estimation tools and spreadsheet automation are deployed in manufacturing and custom fabrication workflows, though some human oversight of novel or complex jobs remains common practice. Production systems exist but may require human validation for highly specialized or one-of-a-kind patternmaking projects. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | General-purpose estimating/quoting software exists but no mature deployed product specifically handles wood patternmaking cost estimation reliably; most craftsmen still estimate manually based on experience. |
Maintain pattern records for reference.
57CI 44–70 · exposure 58 · augmentation 63 · importance 4.1/5 · click for rater detail
Maintain pattern records for reference.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Patternmaking is a lower-digitization, skill-based craft sector; adoption of AI for administrative tasks lags professional services and information industries, with smaller and mid-sized woodworking shops showing slow uptake of digital record systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Small-scale woodworking and traditional patternmaking shops are a low-digitization, physical trade sector with slow AI/software adoption overall. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by suggesting organization schemes, flagging missing metadata, and digitizing paper records, thereby reducing clerical burden while a patternmaker retains judgment over record completeness and technical accuracy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted database and search tools can significantly speed up organizing, tagging, and retrieving pattern records, aiding workers without replacing craft judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI systems can automate portions of pattern record maintenance—cataloging, organizing, and filing existing records digitally—but the task requires domain knowledge about wood patterns, material specifications, and production contexts that demands human oversight to ensure accuracy and completeness. |
| Task automatability | claude-sonnet-5 | 4/5 | Maintaining records (documenting specs, dimensions, revisions, digital filing) is a structured data-management task that AI/database tools can largely automate with proper setup and digitization workflows. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Manufacturing and craft work environments often have established procedures, craftspeople preferences for maintaining their own records, and integration friction with legacy production systems; liability concerns about erroneous patterns affecting manufacturing create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory requirement mandates human record-keeping for wood patterns; it's a low-stakes administrative task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with digitization and organization, the oversight required from a skilled patternmaker to validate records, correct errors, and ensure technical accuracy means the all-in cost (AI + human review) remains comparable to or potentially exceeds direct human filing. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once digitized, software-based record maintenance and retrieval is far cheaper than manual filing and search performed by skilled patternmakers. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document management and record-keeping systems exist and are deployed, but specialized pattern records for woodworking contain technical details (measurements, material notes, tolerances) that current general-purpose systems handle inconsistently without manual verification. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | PLM/PDM software and digital documentation tools are widely deployed in manufacturing, but wood patternmaking shops are often small and may still rely on manual/paper record systems, limiting real-world deployment of AI-driven record-keeping. |
Repair broken or damaged patterns.
36CI 10–62 · exposure 36 · augmentation 25 · importance 4.1/5 · click for rater detail
Repair broken or damaged patterns.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is a small, declining, geographically dispersed craft sector with low digital maturity and minimal capital investment in automation. Adoption of AI for this task is negligible in production; the sector remains largely manual and labor-traditional. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a niche, low-digitization manufacturing trade with minimal AI adoption and no robotics deployment for this specific repair task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision can assist patternmakers by rapidly scanning patterns to highlight damage zones and suggest repair strategies, raising inspection speed and reducing oversight time. However, the creative judgment of how to best restore a complex pattern remains largely human-driven. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical act of diagnosing and repairing damaged wooden patterns using hand tools. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Repairing wood patterns involves assessing damage, proposing fixes, and executing straightforward manual corrections (sanding, filling, replacing sections). Modern vision + 3D CAD systems can detect damage from photos, generate repair plans, and even control CNC machines or robotic arms to execute repairs with high precision and significant time savings over manual patternmaker work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical craft task requiring manual dexterity to repair wooden patterns with woodworking tools; no current AI system can manipulate physical materials to perform such repairs. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensure barriers exist for automating pattern repair; the main friction is customer expectation of human craftsmanship and organizational hesitance to deploy unfamiliar robotic systems in small pattern shops. No legal requirement mandates human sign-off on repairs. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists, but the task requires physical presence, specialized tool use, and tacit craft skill that create substantial practical friction against any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI vision inspection and CNC-assisted repair can approach human labor costs for routine damage, but custom setup, material handling, and oversight for each pattern type keep total costs competitive rather than dramatically cheaper than skilled patternmaker labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical labor, so the human remains the only viable option and cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While vision systems can detect damage and CNC machines exist for woodworking, end-to-end automated repair of varied wood patterns remains immature in production. Current systems handle narrow, well-defined repairs but struggle with the judgment calls and heterogeneous damage types a human patternmaker encounters daily. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical repair of wooden patterns; this remains firmly in the domain of skilled manual craftsmanship. |
Read blueprints, drawings, or written specifications to determine sizes and shapes of patterns and required machine setups.
28CI 23–33 · exposure 25 · augmentation 50 · importance 4.7/5 · click for rater detail
Read blueprints, drawings, or written specifications to determine sizes and shapes of patterns and required machine setups.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is a traditional craft in physical manufacturing sectors with low overall digitization, fragmented small shops, and strong union presence. Adoption of automation tools has historically been slow, and AI-driven blueprint interpretation remains a niche offering in these sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, traditional manufacturing niche with low digitization and slow technology adoption relative to information-sector work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by automating blueprint OCR, flagging dimensions, suggesting preliminary setups, and reducing manual measurement errors. However, the need for spatial judgment, material-specific adjustments, and safety sign-off means AI remains a supporting tool rather than a full productivity transformer. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by highlighting dimensions, flagging inconsistencies, or pre-processing CAD data from blueprints, meaningfully speeding up the interpretive part of the task while the patternmaker still handles setup and craft judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Reading blueprints and interpreting specifications can be partially assisted by AI (OCR, diagram parsing), but determining sizes and shapes and translating them into machine setups requires domain expertise, spatial reasoning, and contextual knowledge that current AI handles only incompletely. Material translation to actionable machine parameters remains largely manual. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision-language models can extract dimensions and specifications from blueprints, but translating this into physical machine setup parameters for wood patternmaking requires tacit craft knowledge and physical verification that current systems cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Skilled patternmakers are typically unionized, and the safety-critical nature of machine setup (cuts, alignment, material waste) creates both liability and regulatory oversight requirements. Custom manufacturing and small shops also demand human judgment on materials and setup variations. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for reading blueprints, but real-world consequences of misinterpretation (wasted material, safety risks in machine setup) create moderate organizational caution before removing human review. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI blueprint parsing and CAD interpretation are relatively inexpensive, but the integration cost, human oversight, and iteration needed to validate machine setup instructions remain substantial compared to a skilled patternmaker's loaded wage for specialized work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Skilled patternmakers command specialized trade wages, but the integration cost of AI systems capable of blueprint interpretation plus machine setup translation, plus required human verification, keeps costs comparable to or above human labor for this niche task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can extract text and basic geometric data from blueprints, no deployed product reliably converts technical drawings into complete, production-ready machine setup instructions without human review. Research systems exist but require significant manual validation and domain expertise. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAD/CAM interpretation tools and some AI-assisted drawing analysis exist, but no deployed product reliably reads varied blueprints and directly configures wood patternmaking machine setups in production settings. |
Verify dimensions of completed patterns, using templates, straightedges, calipers, or protractors.
26CI 19–33 · exposure 20 · augmentation 25 · importance 4.4/5 · click for rater detail
Verify dimensions of completed patterns, using templates, straightedges, calipers, or protractors.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is a traditional, low-volume craft occupation concentrated in small specialized shops with limited digitization. Adoption of AI in this sector remains minimal and slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, low-digitization craft trade with minimal AI adoption momentum reported in industry data. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered measurement assistance (e.g., automated dimension detection from images) could help a patternmaker work faster, but current tools are not widely integrated into workshop workflows and the task is already straightforward with existing manual instruments. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital calipers or CMM-based measurement tools could assist verification, but general AI systems offer little direct assistance to this specific manual quality-control step today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can measure and verify dimensions in images, the task requires physical verification using hand-held tools (templates, calipers, protractors) on actual wooden patterns in a workshop setting. Current AI cannot reliably operate these physical instruments or consistently perform this end-to-end with 50% time savings at equal quality in an uncontrolled environment. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical measurement of a completed wood pattern using hand tools, which current AI systems cannot perform without robotic hardware and vision integration not commonly deployed for this niche task.atab |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There are no strict legal barriers to automating dimension verification, but craft traditions, quality assurance standards, and the need for human judgment on pattern acceptability create organizational friction and preference for human oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific verification step, but it requires physical presence and tactile/visual judgment tied to a skilled trade, creating moderate practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Setting up vision systems, robotic measurement equipment, and integration infrastructure is expensive relative to paying a skilled patternmaker to use simple hand tools for verification. The labor cost of a patternmaker is modest, making the capital and operational cost of AI systems less favorable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative to a skilled patternmaker's manual verification, so no cost comparison favors AI at present. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision can detect and measure geometric shapes in controlled settings, but deploying this to verify wooden patterns with physical tools requires robotics or specialized hardware not yet mature in production workshops. Products exist for industrial quality control but not reliably for this specific manual verification task at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists for physically verifying wood pattern dimensions with calipers or protractors in production woodworking shops; this remains a manual craft task. |
Mark identifying information such as colors or codes on patterns, parts, and templates to indicate assembly methods.
24CI 19–29 · exposure 20 · augmentation 25 · importance 4.1/5 · click for rater detail
Mark identifying information such as colors or codes on patterns, parts, and templates to indicate assembly methods.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Woodworking and patternmaking are small-firm, physically-grounded sectors with low digitization rates. Adoption of robotic marking systems remains minimal, and most shops rely on manual marking practices with little AI integration. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, highly specialized manual trade with minimal digitization or AI adoption occurring in this sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by suggesting appropriate codes or color schemes based on assembly specifications, but the core task of physically marking templates offers limited augmentation potential since the worker must still physically apply marks or verify automated marking regardless. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help generate labeling standards or track coding schemes digitally, but offers little direct assistance for the physical marking act itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Marking physical patterns with colors or codes requires either manual intervention on physical objects or precise computer vision and robotic manipulation. Current AI can identify where marks should go, but actual marking of physical parts remains difficult to automate end-to-end without significant setup and error rates that don't meet the 50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 2/5 | Marking identifying codes requires physical interaction with wooden patterns/templates and knowledge of assembly context, which off-the-shelf AI cannot execute end-to-end without robotic manipulation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements for this marking task, woodworking shops have established workflows and quality standards that create organizational friction around automating marking systems. The need for human verification of correct identification on safety-critical assembly templates adds oversight overhead. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but the physical, tactile nature of marking on handmade wood patterns creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic marking systems with vision integration are capital-intensive and require significant setup, making the all-in cost per task substantially higher than employing a skilled patternmaker to manually mark templates and parts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI substitute exists for physically marking wooden parts, so the human remains the only cost-effective option currently. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can read and classify patterns, no deployed product reliably marks physical wood patterns and templates with identifying information autonomously. The task involves physical manipulation and precise placement on tangible objects in variable environments, which remains research-stage rather than production-ready. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product marks physical wood patterns with assembly codes; this remains a manual, craft-based task performed by skilled patternmakers. |
Finish completed products or models with shellac, lacquer, wax, or paint.
23CI 10–35 · exposure 13 · augmentation 13 · importance 4.2/5 · click for rater detail
Finish completed products or models with shellac, lacquer, wax, or paint.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains limited mainly to large-scale, standardized manufacturing facilities; small patternmakers and custom woodworking remain heavily manual. The sector is not seeing rapid AI/robotics displacement because most work is low-volume and requires human judgment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, highly specialized, low-digitization trade with minimal AI or robotics adoption reported; this is a laggard sector for automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for finishing; vision inspection systems could flag defects, but the core task of applying finish remains manual and judgment-driven. Augmentation potential is low because the work is inherently tactile and aesthetic rather than information-processing based. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the hands-on physical act of applying shellac, lacquer, wax, or paint to a wood model. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While automated spray systems exist for uniform finishing, this task requires judgment about coverage, sheen, application technique, and handling of intricate wood patterns that current AI robotics struggle with reliably. The physical dexterity and visual inspection needed for quality finishing in irregular wooden products falls well short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | Finishing wood patterns/models requires physical manipulation, spray or brush application, and tactile judgment of surface quality that current AI systems cannot perform end-to-end; this requires robotics, not AI software, and no such robotic system is deployed for this niche task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There are some occupational friction and quality standards in custom finishing work, but no hard regulatory barriers preventing automation. Customer preference for hand-finished work and the difficulty of guaranteeing consistent results on varied pieces create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the task demands fine motor skill, material judgment, and physical dexterity in a low-volume craft context, creating practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Setting up and maintaining robotic finishing systems requires significant capital investment and integration costs that often exceed the labor cost for small batch or custom patternmaking work. For irregular pieces requiring customization, human application remains cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical finishing task, so any hypothetical automation (custom robotics) would be far more costly than the skilled human labor involved for this low-volume craft work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic finishing systems are deployed in some high-volume manufacturing, but they work only on standardized, simple geometries. Real patternmaker work involves custom, irregular wooden products where current robots cannot reliably apply finishes without human oversight and frequent manual correction. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product finishes wood patternmaking models with shellac, lacquer, wax, or paint in production settings; this remains a manual craft task. |
Lay out patterns on wood stock and draw outlines of units, sectional patterns, or full-scale mock-ups of products, based on blueprint specifications and sketches, and using marking and measuring devices.
21CI 19–24 · exposure 16 · augmentation 38 · importance 4.5/5 · click for rater detail
Lay out patterns on wood stock and draw outlines of units, sectional patterns, or full-scale mock-ups of products, based on blueprint specifications and sketches, and using marking and measuring devices.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Woodworking, particularly patternmaking, remains a labor-intensive, physical craft with slow digital transformation. Most shops rely on skilled manual labor and traditional methods, with minimal AI or robotic adoption in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Woodworking and patternmaking trades are low-digitization, small-shop-heavy sectors with minimal AI/robotics adoption for this kind of physical layout work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could modestly assist by generating optimized pattern layouts or digitally analyzing blueprints to suggest cut sequences, but the core task of physically laying out and marking patterns on actual wood stock offers limited augmentation opportunity without substantial hardware integration. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD software and digital blueprint interpretation tools can help patternmakers plan layouts more efficiently, though the physical marking on stock still requires human execution. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with some computational aspects of pattern layout (e.g., optimizing cuts), the task requires precise physical marking and measuring on actual wood stock based on detailed blueprints. Current AI systems cannot reliably perform the end-to-end physical manipulation and spatial reasoning needed to achieve 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of wood stock, precise marking with hand tools, and interpretation of blueprints in a physical workspace, none of which off-the-shelf AI can perform end-to-end today.ical. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | The task requires human visual judgment of wood grain, defects, and material variability that affect pattern placement; there is also organizational friction in retrofitting existing woodshops with automation, though no strict licensing requirement prevents automation itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the task requires physical dexterity, workshop presence, and tool use that create practical (not regulatory) barriers to pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized hardware (precision marking and measuring equipment, robot vision integration) required to automate this task would be significantly more expensive than the loaded wage of a skilled patternmaker, particularly for small to medium woodworking shops. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute for physical layout marking, so the human remains necessary and cost comparison favors the human worker for this specific physical action. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products can reliably perform this task autonomously today. Although vision systems and robotics exist in research settings, production-scale autonomous pattern laying and marking on variable wood stock with blueprint fidelity is not demonstrably available in operational woodworking environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical layout and marking on wood stock; CAD/CAM software can generate cut paths but doesn't replace the manual layout task described here. |
Select lumber to be used for patterns.
21CI 15–28 · exposure 8 · augmentation 25 · importance 3.8/5 · click for rater detail
Select lumber to be used for patterns.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is a specialized, often small-scale craft and manufacturing activity with limited digital transformation; adoption of AI in this niche sector lags far behind information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a niche, low-digitization craft trade with minimal AI adoption or investment in automating physical material selection. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by providing wood species identification or historical performance data on pattern materials, but the core task of tactile assessment and judgment remains largely human-driven, limiting augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could theoretically assist with inventory tracking or defect-flagging via computer vision, but no meaningful augmentation tool is in common use for this specific selection task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Selecting lumber for patterns requires evaluating wood quality, grain, moisture content, and suitability for specific pattern shapes—tasks involving spatial reasoning and material knowledge that current AI systems struggle with without specialized 3D sensing and material databases. While AI could assist with some criteria (wood type matching), the full assessment of physical properties and end-use fit remains heavily dependent on human tactile and visual expertise. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically selecting and inspecting lumber for grain, defects, warping, and suitability for woodworking patterns requires hands-on sensory judgment that current AI cannot perform end-to-end.imo |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers, the task requires hands-on material inspection and expert judgment that organizations typically delegate to experienced workers; customer expectations and quality liability create modest friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the physical nature of handling and inspecting lumber stock creates a practical barrier to any current automation, robotic or AI-driven. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current computer vision and material analysis systems would require significant integration costs, imaging hardware, and ongoing training data curation, making them comparable to or more expensive than the straightforward cost of a skilled patternmaker making selections manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical selection task, so cost comparison favors the human by default since AI cannot execute it at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs wood selection for patternmaking in production today. Computer vision systems exist for lumber grading, but they are narrow in scope, require controlled imaging environments, and do not make the nuanced judgment calls about pattern-specific suitability that this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously selects physical lumber stock for patternmaking; this remains a manual craft task requiring physical presence and tactile/visual inspection. |
Issue patterns to designated machine operators.
21CI 15–28 · exposure 8 · augmentation 25 · importance 3.3/5 · click for rater detail
Issue patterns to designated machine operators.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Woodworking and pattern-making are traditional, physically-grounded trades with low overall digital adoption. Most patternmaker shops remain small, on-site operations without the infrastructure or incentive to digitize this basic coordination task. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small-scale, low-digitization manufacturing niche with minimal AI adoption reported.5.0 |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | A simple pattern management or queue system could assist by organizing which patterns are ready and which operators are available, but the task itself is already minimal and does not benefit significantly from AI enhancement in typical small-shop settings. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, inventory tracking, or digital pattern management, but offers little assistance to the physical act of issuing patterns.5.0 |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Issuing patterns to machine operators is a coordination and communication task that requires human judgment about which operator gets which pattern based on skill, availability, and scheduling context. Current AI systems cannot reliably manage this decision-making and physical handoff without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Issuing physical wood patterns to operators involves physical handoff and coordination on a shop floor, which current AI cannot perform end-to-end; only scheduling/tracking aspects could be lightly automated.5.0 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict legal licensing requirements for pattern issuance itself, the task sits within a skilled tradecraft context where human judgment about operator capability and material fit is valued, creating some organizational friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but the physical nature of the task and need for coordination with operators creates practical friction against pure AI substitution.5.0 |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is fundamentally a brief coordination activity; automating it would require integrating multiple systems (pattern tracking, operator scheduling, communication) at a cost likely exceeding the minimal human time already spent on issuance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Any automation would require robotics or physical logistics systems that are costly relative to a human simply carrying and handing off a pattern.5.0 |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs the social and logistical coordination of pattern distribution in woodworking shops. This task involves human-to-human communication, physical verification, and real-time scheduling decisions that production systems do not handle autonomously. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product manages physical distribution of wood patterns to machine operators in production woodworking shops today.5.0 |
Set up, operate, and adjust a variety of woodworking machines such as bandsaws and lathes to cut and shape sections, parts, and patterns, according to specifications.
18CI 10–25 · exposure 8 · augmentation 25 · importance 4.3/5 · click for rater detail
Set up, operate, and adjust a variety of woodworking machines such as bandsaws and lathes to cut and shape sections, parts, and patterns, according to specifications.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Woodworking and pattern-making remain largely in small, physically-intensive shops with low digitization; while CNC adoption exists in larger facilities, the broader sector shows slow, uneven automation compared to information and finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Small-scale woodworking and patternmaking shops are a low-digitization, physical craft sector with minimal AI adoption; CNC automation has existed for decades but is distinct from generative AI adoption patterns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with design verification or cut-planning, but the core task—live operation and physical adjustment of machines—offers limited augmentation opportunity because real-time sensorimotor feedback and safety override remain firmly human. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with CAD/CAM design and toolpath generation prior to machine operation, but offers little real-time assistance during the actual physical cutting and shaping process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically plan cuts and shapes, the task requires real-time physical operation and adjustment of woodworking machines in response to material variation, tool wear, and safety—currently infeasible for general-purpose robotic systems in unstructured shop environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of woodworking machinery, precise manual dexterity, and real-time sensory feedback (sight, sound, touch) that current AI systems cannot replicate end-to-end without robotic embodiment far beyond off-the-shelf availability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Safety regulations, operator licensing expectations, and liability concerns for machine operation create moderate friction, though no explicit legal mandate requires a human operator in all jurisdictions; organizational resistance to automation is significant in small shops. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically, but safety regulations, liability for machine operation, and the skilled trade nature of setup and adjustment create meaningful organizational and safety-based friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Woodworking automation equipment (CNC or specialized robots) is capital-intensive and requires setup; the all-in cost per task equivalent remains higher than a trained patternmaker's loaded wage for flexible, varied work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system performing this physical task, so any comparison defaults to AI being effectively infinitely costlier since it cannot substitute at all without specialized robotics investment far exceeding human wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably sets up, operates, and adjusts diverse woodworking machines (bandsaws, lathes) end-to-end in production. Specialized industrial robotics exist for narrow, repetitive tasks but lack the adaptability required here. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product operates bandsaws or lathes autonomously for patternmaking; CNC machines exist but require pre-programmed toolpaths and human setup/adjustment, not AI-driven adaptive operation. |
Construct wooden models, templates, full scale mock-ups, jigs, or molds for shaping parts of products.
18CI 5–30 · exposure 8 · augmentation 38 · importance 4.0/5 · click for rater detail
Construct wooden models, templates, full scale mock-ups, jigs, or molds for shaping parts of products.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of CAD/CNC in woodworking and pattern shops is moderate and uneven, concentrated in larger manufacturers. Many shops remain semi-manual, and custom patternmaking is slow to adopt autonomous systems due to low volumes and high variability. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, highly manual, low-digitization trade with minimal AI agent deployment; CNC and CAD tools have existed for decades but are not AI-driven automation of this specific craft task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD tools, design libraries, and CNC simulation can assist patternmakers in drafting and planning, reducing layout time and catching design errors early. However, the core physical construction and iterative refinement remain human-centered. |
| Augmentation potential | claude-sonnet-5 | 2/5 | CAD/CAM software and generative design tools can assist in planning dimensions and layouts before physical construction, offering some productivity benefit, but the hands-on building process itself receives little AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CAD/CAM systems can generate digital models and CNC machines can cut some template components, the task requires spatial judgment, material-specific adjustments, and iterative physical refinement that current AI cannot perform end-to-end. Building functional jigs or molds from scratch remains heavily dependent on tacit craft knowledge and hands-on testing. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical hand-crafting task requiring manual woodworking skills, tool operation, and tactile judgment that current AI systems cannot perform end-to-end; AI has no manipulator capability to construct physical wooden objects. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task requires hands-on fabrication, physical problem-solving, and material expertise that cannot be outsourced to purely digital systems. Regulatory and safety considerations in manufacturing, combined with the need for on-site adjustment and validation, create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human patternmaker, but the physical nature of shaping wood and using specialized tools creates a hard practical barrier since no AI system can execute the manual fabrication. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | CNC equipment and CAD software have high capital and integration costs, while patternmakers command skilled wages. For small batches or one-off custom work, the setup and overhead often exceed the cost of human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical fabrication, so any AI cost is irrelevant to producing the actual output; the human (or CNC machinery, not AI) remains the only viable cost path. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably constructs physical wooden models, molds, or jigs autonomously. CNC machines exist but require expert human programming, design, and material-handling decisions; they are tools, not autonomous agents for this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product constructs wooden models, templates, or molds; this remains squarely a human craft skill with only CNC/CAD-assisted design as adjacent automation, not the physical construction itself. |
Fit, fasten, and assemble wood parts together to form patterns, models, or sections, using glue, nails, dowels, bolts, and screws.
15CI 15–15 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Fit, fasten, and assemble wood parts together to form patterns, models, or sections, using glue, nails, dowels, bolts, and screws.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Wood patternmaking is a specialized, small-scale, low-digitization craft sector with little evidence of AI or robotic adoption in production settings; operators tend to work in traditional manufacturing contexts resistant to automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, low-digitization craft trade with minimal AI/robotics adoption; the sector shows negligible movement toward automated physical assembly. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with design planning and part layout optimization, but offers minimal support during the hands-on fitting, fastening, and assembly phases where tactile feedback and real-time problem-solving dominate. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with CAD-based pattern design or CNC-guided cutting instructions beforehand, but offers little direct help during the manual fitting, gluing, and fastening process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation, spatial reasoning under varied conditions, and real-time adjustments with hand tools and fasteners in a three-dimensional woodworking environment. Current AI systems lack embodied robotic capabilities to perform end-to-end gluing, fastening, and assembly at equal quality and speed. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical hand-assembly task requiring fine motor manipulation, fitting judgment, and tool use with wood; no current AI system (software-based) can perform physical fastening/assembly, and robotics for bespoke woodworking is far from off-the-shelf capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for the task itself, the need for real-time quality judgment, custom fitting, and liability for structural integrity creates moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but physical dexterity, craftsmanship judgment, and non-standardized workpieces create strong practical barriers to substitution even though not regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and integration cost of robotic systems capable of flexible wood assembly, combined with frequent reconfiguration for different patterns and designs, far exceeds the loaded wage of skilled patternmakers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so any hypothetical automation would require expensive custom robotics far costlier than a skilled patternmaker's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs the full spectrum of wood pattern fitting, fastening, and assembly without human oversight. Robotic woodworking systems exist only in research settings with highly constrained, pre-designed tasks. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs custom wood pattern assembly; robotic woodworking remains research/prototype stage for standardized parts, not bespoke fitting and fastening of irregular pattern pieces. |
Trim, smooth, and shape surfaces, and plane, shave, file, scrape, and sand models to attain specified shapes, using hand tools.
15CI 15–15 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Trim, smooth, and shape surfaces, and plane, shave, file, scrape, and sand models to attain specified shapes, using hand tools.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is concentrated in small, traditional craft shops and specialized manufacturing that are not early adopters of automation. Digitization and AI adoption in this sector remain minimal and slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, highly specialized manual trade with minimal digitization or AI adoption; it lags far behind information-sector AI adoption patterns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by generating or simulating shape specifications or visualizing 3D targets, but provides minimal help with the core sensorimotor task of hand-tool shaping. The worker remains dependent on their own hands and feel. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers little direct assistance to the physical shaping process itself, though CAD/CAM design tools upstream may indirectly inform pattern specifications. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of wood using hand tools in three-dimensional space with real-time tactile feedback. Current AI and robotics cannot reliably perform the iterative sensorimotor work of planing, shaving, filing, scraping, and sanding to attain specified shapes at human quality levels. |
| Task automatability | claude-sonnet-5 | 1/5 | This is fine-motor, tactile handwork with hand tools requiring physical dexterity and real-time judgment of shape and surface; no current AI system (including robotics) can perform this end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task is not explicitly licensed, but it requires substantial human judgment about wood properties, material response, and aesthetic/functional specification interpretation. There is no legal requirement for human sign-off, though the craft nature and bespoke demand create organizational and quality-control friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandates a human specifically, but the task's reliance on tactile skill and craftsmanship creates strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of wood shaping are capital-intensive and require significant setup; their per-unit cost far exceeds a skilled patternmaker's labor, especially for one-off or small-batch custom work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical craft task, so any hypothetical automated solution (custom robotics) would be far more costly than a skilled patternmaker's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this task end-to-end. While robotic woodworking exists in narrow, highly controlled manufacturing contexts, general-purpose wood patternmaking with hand-tool finesse remains a human craft with no production AI alternative. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform hand-tool trimming, shaping, and sanding of wood patterns; this remains a research-stage robotics/manipulation problem, not a commercial offering. |
Glue fillets along interior angles of patterns.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Glue fillets along interior angles of patterns.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is a small, declining, traditionally-organized sector with low digital integration and heavy reliance on skilled craftspeople; adoption of robotics or AI for fine manual work remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, highly specialized, low-digitization trade with virtually no AI or robotics adoption occurring in this niche manufacturing craft. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance for the physical act of gluing fillets; even vision systems for guidance would require solved robotics and provide minimal productivity gain over skilled human judgment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this tactile, precision hand-gluing step, as it involves no data processing, drafting, or decision-making that current AI tools address. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Gluing fillets requires fine manual dexterity, precise spatial judgment within complex 3D wooden patterns, and real-time tactile feedback to ensure proper adhesion and alignment. Current AI systems lack the embodied manipulation capabilities and sensorimotor control needed for this physical task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine manual manipulation task requiring physical dexterity, spatial judgment, and hand-eye coordination to place and glue small wood fillets into precise interior corners—no off-the-shelf AI system can perform this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing or legal barriers preventing automation, the craft nature of patternmaking and customer expectations for human-made precision patterns create organizational and market friction against replacement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but the task requires specialized craft skill and fine motor control that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of precision fillet work (if they existed at scale) would cost far more than the loaded wage of a skilled patternmaker, making automation economically infeasible for this craft-level task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven robotic solution for this precise manual gluing task, so any hypothetical automation would require expensive custom robotics far costlier than a skilled patternmaker's labor for this narrow task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI system reliably performs fillet gluing on wooden patterns at production scale. This requires coordinated physical manipulation in unstructured environments, which remains largely in research and specialized industrial robotics, not general-purpose deployed products. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical fillet gluing on wooden patterns; this remains a craft task done entirely by skilled human hands with no robotic automation in production for this niche trade. |
Collect and store patterns and lumber.
15CI 15–15 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Collect and store patterns and lumber.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Woodworking and patternmaking remain relatively low-digitization sectors with limited capital investment in automation; adoption of robotics for material handling is laggard. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking and manufacturing are low-digitization, physical-labor-intensive sectors with minimal AI/robotics adoption for basic material handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with inventory tracking or pattern cataloging via computer vision or database systems, but offers minimal augmentation for the core physical collection and storage activities. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with inventory tracking or organizing storage logs digitally, but offers little direct assistance to the physical collection and storage motion itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of materials (patterns and lumber) in a workshop environment, requiring coordination, spatial reasoning, and manual handling that current AI systems cannot perform end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task involving handling and storing wooden patterns and lumber, which requires manipulation of physical objects and warehouse navigation that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements, the physical, on-site nature of the work and the need for judgment about proper storage conditions create modest organizational friction to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement mandates a human for this task, but physical workspace constraints and lack of robotic infrastructure create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of robotics or autonomous systems capable of handling, transporting, and storing wood materials and patterns would far exceed the labor cost of a human patternmaker performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only solution for this physical handling task, so any AI-adjacent approach (e.g., robotics) would be far more expensive than a human worker performing routine warehouse tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously collect, organize, and store physical patterns and lumber at production scale; robotics for this domain remain specialized and not widely available in woodworking shops. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously collects and stores wood patterns and lumber in production; this would require robotic manipulation and mobility that remains research-stage for such varied physical materials. |
Divide patterns into sections according to shapes of castings to facilitate removal of patterns from molds.
14CI 5–23 · exposure 8 · augmentation 25 · importance 4.4/5 · click for rater detail
Divide patterns into sections according to shapes of castings to facilitate removal of patterns from molds.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Pattern making is concentrated in legacy manufacturing and foundry sectors with low digitization rates and strong craft traditions. Adoption of advanced automation in this occupation remains minimal, and the small, dispersed population of patternmakers limits network effects for any new technology. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, low-digitization manufacturing niche with minimal AI adoption; robotics/CAD tools exist for some casting design but not this specific manual decomposition step. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools might assist with visualization of candidate section divisions or constraint checking, but the core creative and spatial reasoning judgment required to divide patterns remains best performed by experienced humans. Limited potential for transformative human productivity gain in this task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | CAD/CAM software and 3D modeling tools can assist in planning pattern sections and simulating mold removal, offering some productivity benefit, but the physical execution and judgment remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires spatial reasoning about 3D casting geometry and mold mechanics to decompose patterns optimally. While AI can process 2D images and analyze shapes, end-to-end automation of dividing complex 3D patterns into removal-friendly sections with the precision and domain expertise patternmakers apply remains beyond reliable current capability. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation and expert judgment about mold geometry and material behavior when working with actual wooden patterns; no AI system performs this physical, spatially-embodied craft task today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Patternmakers are typically union-affiliated craftspeople in manufacturing, and the critical role of pattern design in casting quality creates organizational and skill-based friction against automation. Liability concerns over casting failures traceable to poor pattern decomposition further protect the human role. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists, but the task requires physical tool use, spatial reasoning about mold-casting interactions, and craft experience that create substantial practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference and CAD integration costs would likely exceed the hourly loaded wage of a skilled patternmaker when accounting for the high oversight and error-correction demands; accuracy failures in pattern division carry expensive downstream casting defects. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that substitutes for this physical task, so the human wage remains the only viable cost, making AI more expensive by default (nonexistent capability). |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform this specialized task in production. This is primarily a craft-based activity requiring deep domain knowledge of foundry processes and material behavior, with only exploratory research-stage approaches emerging in CAD/ML integration. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this specialized woodworking/pattern-making decomposition task; it remains a manual craft skill exercised in small-scale foundry pattern shops. |
Correct patterns to compensate for defects in castings.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Correct patterns to compensate for defects in castings.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is a niche, low-digitization sector with small shops and traditional craft-based processes; foundries and pattern shops have lagged in AI adoption and lack the infrastructure for automated pattern correction. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Wood patternmaking is a small, highly manual, low-digitization trade within foundry/manufacturing sectors that show minimal AI adoption for physical craft tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Vision-based defect detection could modestly assist patternmakers in identifying problem areas, but the correction itself—a highly specialized geometric and material-science task—offers limited augmentation potential with current AI tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with defect analysis or shrinkage calculations via simulation software, but it offers little direct help with the hands-on pattern correction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Correcting patterns to compensate for casting defects requires domain-specific expertise, visual inspection of three-dimensional physical objects, and iterative adjustments based on metallurgical knowledge. Current AI cannot reliably perform the full cycle of defect analysis and pattern correction on physical castings end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical inspection of castings, hands-on woodworking skill to modify a physical pattern, and tacit craft judgment about shrinkage/defect compensation—no current AI system can execute this physical correction task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task requires a licensed patternmaker with specialized technical credentials; foundries have strict quality assurance protocols and liability concerns around defective castings, creating regulatory and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law mandates a human specifically, but the task requires physical dexterity, specialized craft knowledge, and immediate on-site judgment that create strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of acquiring imaging systems, training models for casting defect classification, integrating with CAD/pattern software, and human oversight would exceed the loaded hourly wage of a skilled patternmaker performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that performs this physical task at all, so AI cost cannot be favorably compared—human labor is the only viable option today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs this task in production settings. While vision systems can detect some casting defects, pattern correction requires specialized mechanical knowledge and physical manipulation that current systems do not integrate. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical woodworking pattern correction; this remains a manual skilled-trade activity with no commercial AI system addressing it. |
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.