Commercial and Industrial Designers
27-1021.00Median wage $83,910/yr33,490 employed (US)Rank #230 of 923 scored · top 25% by substitution
Design and develop manufactured products, such as cars, home appliances, and children's toys. Combine artistic talent with research on product use, marketing, and materials to create the most functional and appealing product design.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution37
Exposure32
Augmentation75
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
17 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
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%34
panel mean rating 2.3/5 → substitution pressure 34/100
Technical feasibility todayw 20%29
panel mean rating 2.1/5 → substitution pressure 29/100
Cost vs. human wagew 15%36
panel mean rating 2.5/5 → substitution pressure 36/100
Adoption barriersw 20%inverted — strong barriers lower the score52
panel mean rating 2.9/5 (barrier strength) → substitution pressure 52/100
Sector adoption velocityw 10%34
panel mean rating 2.4/5 → substitution pressure 34/100
Task breakdown (17 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Read publications, attend showings, and study competing products and design styles and motifs to obtain perspective and generate design concepts.
68CI 59–77 · exposure 62 · augmentation 100 · importance 3.1/5 · click for rater detail
Read publications, attend showings, and study competing products and design styles and motifs to obtain perspective and generate design concepts.
68| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Design and creative sectors show moderate AI adoption—generative tools and trend analysis are increasingly piloted in professional services, but human creativity remains culturally valued and production deployment is still piecemeal rather than systematic. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Design and creative industries are adopting generative AI tools at a moderate pace, with growing pilot and production use in ideation but not yet universal integration into workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments human designers by rapidly synthesizing competitive landscapes, surfacing design motifs, and generating mood boards; designers stay in the loop for aesthetic judgment and originality while AI accelerates research throughput. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly enhances research efficiency and concept brainstorming by quickly surfacing trends, competitor products, and generating visual design variations for designers to refine. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can autonomously scan publications, analyze product images, extract design elements, and synthesize design trends with minimal human oversight, saving well over 50% of the time a human designer would spend on research and concept generation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly synthesize trend reports, summarize publications, and generate mood boards or design concepts from prompts, but genuine attendance at showings and physical/tactile perspective-gathering cannot be automated.the research/synthesis half is largely automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or regulatory barriers; designers are not licensed and no statute requires human sign-off on research or ideation. Organizational friction exists (preference for human taste and originality) but does not prevent substitution. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human perform trend research or concept ideation; this is a creative/informational task with low structural barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven research and concept synthesis costs (cloud APIs, integration overhead) are substantially cheaper than paying a designer to manually review publications, attend showings, and synthesize competitive analysis, likely 3–10× cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted research and concept generation tools are inexpensive per output compared to a designer's time spent manually reviewing publications and showings, though not fully order-of-magnitude cheaper since human synthesis still required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (image recognition APIs, trend-analysis tools, generative design platforms) reliably perform core subtasks—visual analysis, trend extraction, competitive product comparison—though final concept refinement typically requires human oversight in production use. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like generative design tools, trend-analysis AI, and image generators (Midjourney, Adobe Firefly) are used in production for concept generation and competitive research, though with narrow scope and human curation needed. |
Participate in new product planning or market research, including studying the potential need for new products.
64CI 41–87 · exposure 58 · augmentation 88 · importance 3.3/5 · click for rater detail
Participate in new product planning or market research, including studying the potential need for new products.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Design and product development teams in tech, consumer goods, and professional services are rapidly integrating AI for market insights, trend analysis, and idea generation; production adoption is accelerating in information-intensive sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Design and product development functions are adopting AI research and ideation tools at a moderate pace, with pilots common but full integration into planning workflows still developing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments designer productivity by rapidly synthesizing market data, identifying patterns, generating scenario analyses, and surfacing customer insights, allowing human designers to focus on strategic interpretation and creative synthesis. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly speed up literature reviews, trend analysis, and initial concept generation, meaningfully boosting designer productivity during the planning phase. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can conduct market analysis, trend forecasting, competitive research, customer need identification, and feasibility studies—the core components of product planning and market research—at significant scale and speed, easily meeting the 50% time-saving threshold with current large language models and data analytics tools. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can synthesize research and generate ideas, but genuine market discovery, stakeholder negotiation, and strategic judgment about unmet needs require human-led inquiry and validation that current systems cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent AI-assisted market research; organizational friction exists (stakeholder preference for human judgment in strategic decisions) but does not prevent automation of the research and analysis phase itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational reliance on designer intuition, client trust, and iterative human judgment creates moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven market research and planning analysis costs a fraction of hiring human researchers or consultants; inference plus integration is orders of magnitude cheaper than the loaded cost of professional planners for equivalent research scope. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply generate background research and trend reports, but the human synthesis, primary research, and validation still needed keeps overall cost roughly comparable to traditional methods. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed AI products (market research platforms, generative AI for insights extraction, data analysis tools) perform substantial portions of this task reliably in production, though final strategic recommendations typically require human judgment and domain expertise to validate findings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like AI market research summarizers and trend analysis tools exist but are narrow, often producing generic or unverified insights that require heavy human vetting before use in product planning. |
Design graphic material for use as ornamentation, illustration, or advertising on manufactured materials and packaging or containers.
58CI 55–61 · exposure 50 · augmentation 88 · importance 3.2/5 · click for rater detail
Design graphic material for use as ornamentation, illustration, or advertising on manufactured materials and packaging or containers.
58| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Adoption is accelerating in marketing, advertising, and packaging design—sectors with high digitization and cost pressure. Major agencies and in-house teams are integrating AI design tools into workflows; pilots are common and production use is growing, particularly for rapid prototyping and lower-stakes materials. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Design and creative industries have seen notable AI tool adoption for ideation and drafting, but many firms still use AI as a supplementary tool rather than replacing designers in production workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI design tools significantly augment human designers by generating concept variations, speeding iteration, and handling routine layout and color work, allowing designers to focus on strategic direction and refinement. This raises overall productivity while keeping creative judgment and brand stewardship in human hands. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI image generation tools substantially speed up ideation, moodboarding, and concept exploration for graphic and packaging design, letting designers iterate far faster while retaining creative control. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate candidate graphic designs, layouts, and color schemes for packaging and advertising materials with reasonable speed, but human designers typically need to refine, iterate, and adapt outputs to brand guidelines, target audiences, and specific manufacturing constraints. This covers roughly half the design workflow with significant setup for prompts and style parameters. |
| Task automatability | claude-sonnet-5 | 3/5 | AI image generation tools can produce draft graphic designs, patterns, and packaging visuals quickly, but final production-ready artwork still requires human refinement for brand consistency, print specs, and client approval.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal licensing requirement exists for graphic design in most jurisdictions, and client preference for human creativity is weakening as AI design tools become normalized. Organizational adoption is mainly voluntary, though some firms maintain creative teams for premium positioning. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for graphic design work, though brand/IP considerations and client preference for human creative judgment create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference costs for design generation are low (dollars per design variant), whereas hiring a human designer or outsourcing design work costs hundreds to thousands per project. Even with human oversight, the cost per design output is substantially lower than traditional labor. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI generation is cheap per image, but the full workflow including revisions, technical adaptation, and quality control still requires paid designer time, making overall cost roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI image generation and design tools (DALL-E, Midjourney, Adobe Firefly) are in production and used by design teams, but outputs often require substantial human correction for text legibility, brand consistency, and manufacturing feasibility. Narrow scope and material error rates in adherence to specifications limit reliability for end-to-end task completion. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Adobe Firefly, Midjourney, and Canva's AI tools are deployed and used by designers for ideation and mockups, but reliable end-to-end production of print-ready packaging graphics still has notable error rates in text accuracy, brand fidelity, and technical specs. |
Research production specifications, costs, production materials, and manufacturing methods and provide cost estimates and itemized production requirements.
53CI 47–59 · exposure 45 · augmentation 75 · importance 3.7/5 · click for rater detail
Research production specifications, costs, production materials, and manufacturing methods and provide cost estimates and itemized production requirements.
53| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Design-adjacent sectors (manufacturing, automotive, CPG) are experimenting with AI-assisted specification and cost tools, but adoption remains in pilot and early-deployment phases rather than widespread production use. Larger consultancies are testing these tools, but SMEs and traditional design firms lag significantly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Industrial design and manufacturing sectors have moderate digitization but are not among the fastest AI-adopting fields, with most AI use still limited to pilots or generative design assistance rather than deep production cost analysis. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment designer productivity by rapidly compiling material options, pulling real-time supplier pricing, and auto-generating itemized cost tables and BOMs, allowing designers to focus on optimization and trade-off decisions rather than manual research. This assistive role is actively realized in current design software and emerging AI tools. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up research into materials, costs, and manufacturing options, helping designers compile itemized cost estimates faster, even though final numbers require human verification. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate data gathering on production specifications, costs, and materials from databases and supplier catalogs, and generate preliminary cost estimates and itemized lists with moderate setup. However, the task requires contextualized judgment about material trade-offs, supplier reliability, and manufacturing feasibility that typically demands designer expertise, making full end-to-end automation below the 50% time-saving threshold for high-quality outcomes. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can gather and synthesize much of the research on materials, costs, and manufacturing methods and draft cost estimates, but validating supplier-specific pricing and finalizing itemized requirements still requires human judgment and verification. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers prevent AI from performing cost research and estimation; however, organizational practice and designer judgment preferences create moderate friction, and liability concerns around cost accuracy may slow adoption in some sectors. No hard regulatory requirement mandates human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There is no licensing requirement for this task, though organizational reliance on accurate vendor quotes and internal approval processes creates some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Inference cost for material lookup, cost aggregation, and estimate generation is very low (pennies per task), while a designer-level professional performing this research and estimation charges $50–150+ per hour. The cost ratio heavily favors AI once systems are deployed, even accounting for oversight and error correction. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted research can cut significant time versus manual lookup, but human review, supplier verification, and negotiation still add substantial cost, making the total cost roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (e.g., AI-assisted cost estimation tools, material databases, and LLM-based specification retrieval) that perform parts of this task in production environments, but they typically require significant designer validation and have material error rates in cost accuracy and manufacturing feasibility assessment. Fully autonomous cost estimation across novel designs remains unreliable. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generic LLM tools can produce draft research summaries and rough cost breakdowns, but no mature deployed product reliably generates accurate, up-to-date manufacturing cost estimates and itemized production requirements at scale in design workflows. |
Direct and coordinate the fabrication of models or samples and the drafting of working drawings and specification sheets from sketches.
52CI 35–70 · exposure 45 · augmentation 88 · importance 3.5/5 · click for rater detail
Direct and coordinate the fabrication of models or samples and the drafting of working drawings and specification sheets from sketches.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Design and manufacturing sectors are actively adopting generative design and AI-assisted CAD tools; major aerospace, automotive, and industrial design firms have deployed or are piloting these systems in production workflows. Adoption is faster in digitally mature, capital-intensive industries with high-volume design iteration cycles. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Industrial design and manufacturing-adjacent sectors have slower AI adoption compared to pure information work; CAD/generative tools are used but full workflow automation is uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments human designers by automating tedious geometric drafting, constraint satisfaction, and specification documentation, freeing them to focus on creative intent, design validation, and strategic decision-making. Designers using these tools report substantial productivity gains while remaining fully in control of design outcomes and iterations. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted drafting, generative CAD, and automated specification generation significantly speed up producing working drawings and spec sheets from sketches, meaningfully augmenting designer productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can automate a significant portion of this task: CAD-integrated generative design tools can convert sketches and specifications into working drawings and 3D models with substantial time savings, and fabrication planning software can coordinate model production workflows. However, human judgment on design intent, material selection, and fabrication feasibility constraints typically requires some human review, preventing a fully autonomous end-to-end execution without oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | The coordination and directing of physical fabrication and cross-team drafting workflows requires managerial judgment, physical inspection, and iterative human feedback that current AI cannot perform end-to-end. AI can assist drafting sub-steps but not the directing/coordinating function itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard regulatory or licensing barriers exist for AI-assisted drafting and model generation; no legal requirement mandates that a licensed designer must sign the final drawing (though professional practice expectations and liability often push firms to retain human review). Organizational friction (design teams preferring human oversight, quality assurance processes) provides some protection but not a binding constraint. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically blocks this, but organizational structure requires a responsible human to interface with fabricators and sign off on specifications, creating moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven CAD and drafting tools have low marginal inference costs and can generate drawings orders of magnitude faster than manual drafting. Integration and oversight overhead exist but remain modest relative to the labor hours saved on repetitive geometric and specification documentation work, making AI substantially cheaper per output unit than a human designer's time. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI drafting tools reduce some drawing time cheaply, but the managerial/coordination portion still requires paid human oversight, keeping overall cost roughly comparable to a designer's time when including supervision of the physical fabrication. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Mature CAD systems with AI-assisted drafting (e.g., Fusion 360, Solidworks with generative design plugins) exist in production at scale, and sketch-to-CAD conversion tools are deployed in some design firms. However, these systems require structured inputs, lack perfect reliability in translating complex design intent, and often need manual refinement—limiting them to semi-autonomous rather than fully reliable independent execution. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAD-adjacent and generative design tools exist, but no deployed product manages fabrication coordination or directs a team producing physical models/samples reliably in production. |
Prepare sketches of ideas, detailed drawings, illustrations, artwork, or blueprints, using drafting instruments, paints and brushes, or computer-aided design equipment.
51CI 46–55 · exposure 50 · augmentation 88 · importance 4.4/5 · click for rater detail
Prepare sketches of ideas, detailed drawings, illustrations, artwork, or blueprints, using drafting instruments, paints and brushes, or computer-aided design equipment.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Design and creative sectors show growing pilot adoption of generative AI and CAD tools, but most firms still use these as assistive rather than replacement-level systems. Enterprise CAD software is embedding AI features, but widespread production-level substitution is still emerging rather than established. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Design and product development fields are adopting generative AI tools for ideation at a moderate pace, with pilots and partial integration common but full production blueprint automation still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists designers by rapidly generating concept variations, automating repetitive technical drawing tasks, and accelerating iteration cycles. Designers using these tools can explore more options faster, making this a high-value augmentation while the designer remains in creative control. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically speeds up ideation, mood-boarding, and rough concept generation, letting designers explore more variations quickly while retaining control over final detailed output. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate initial sketches and illustrations from text prompts and automate parts of technical drawing via CAD software integration, but requires significant human direction, iteration, and refinement to meet professional design standards. The creative decision-making and detailed specification work still demand substantial human involvement. |
| Task automatability | claude-sonnet-5 | 3/5 | AI image/CAD generation tools can quickly produce concept sketches and illustrations, but detailed manufacturable drawings and blueprints still require substantial human specification, iteration, and technical precision beyond current generative capabilities. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No strict licensing requirement for the automation itself, but professional liability, client expectations for human creativity/accountability, and organizational inertia in design firms create meaningful friction. Clients often demand human sign-off and ownership of creative output. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted design work, though liability for functional/safety-critical blueprints and client expectations for human craftsmanship create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference costs for image generation are now low, but integration with design workflows, quality oversight, and human correction still consume significant labor. The all-in cost per usable final design remains comparable to or slightly below hiring junior designers rather than substantially cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-generated concept art is cheap per image, but achieving usable, revision-ready detailed drawings requires designer oversight and rework, narrowing the cost advantage for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Generative AI tools (DALL-E, Midjourney, Stable Diffusion) and CAD-integrated AI features exist in production, but produce variable quality and often require manual cleanup, redrawing, or major revisions. These systems work best for ideation rather than final deliverables. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Midjourney, DALL-E, and generative CAD plugins are used in production for early concept ideation, but detailed technical drawings/blueprints for manufacturing still rely on traditional CAD software with human-driven precision. |
Fabricate models or samples in paper, wood, glass, fabric, plastic, metal, or other materials, using hand or power tools.
35CI 15–55 · exposure 25 · augmentation 50 · importance 3.0/5 · click for rater detail
Fabricate models or samples in paper, wood, glass, fabric, plastic, metal, or other materials, using hand or power tools.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | CNC and 3D printing adoption is moderate in design firms and product development shops, with many organizations piloting or selectively deploying equipment; widespread adoption is tempered by high capital costs and the continued value of skilled human craftsmanship in premium design contexts. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Physical prototyping and model-making in design fields show minimal AI adoption since the task is inherently manual and tactile, unlike digital design tasks in the same occupation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Digital fabrication tools substantially augment designer productivity by enabling rapid prototyping, accurate reproductions, and exploration of complex geometries while designers remain in control of material choice, design refinement, and quality judgment; this human-in-the-loop augmentation is a core workflow in modern design practice. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with generating design specifications, CAD files, or 3D printing instructions beforehand, but it offers little direct assistance during the hands-on fabrication process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Partial automation is feasible for elements like cutting, shaping, and assembly of models via CNC machines, 3D printing, or robotic systems; however, the aesthetic refinement, material selection judgment, and quality inspection still require significant human oversight, preventing full end-to-end automation with 50% time savings uniformly. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication task requiring manual dexterity, tool operation, and hands-on material manipulation that current AI systems cannot perform without robotic embodiment, which is not generally available for this kind of skilled craft work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for operating fabrication equipment, and designers typically own or control the equipment; organizational friction comes mainly from setup time and training rather than legal or compliance requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, but physical tool use, safety considerations, and the need for hands-on craftsmanship create practical friction against any non-human approach today. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Capital equipment and software licensing for digital fabrication systems rival or exceed the loaded wage of a skilled model maker in lower-volume settings, though in high-volume production environments the cost per unit can favor automation; integration and material costs add complexity to the calculation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so cost comparison favors the human by default; any robotic fabrication solution would be far more expensive and less flexible than a skilled designer. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | CNC machines, laser cutters, and 3D printers are deployable in production environments for model fabrication, but they require human setup, material expertise, and post-processing; they handle routine geometric work reliably but struggle with complex material combinations and finish quality that designers demand. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product fabricates physical models or prototypes using hand or power tools; this remains firmly in the domain of human craftsmanship and specialized fabrication equipment operated by people. |
Coordinate the look and function of product lines.
29CI 25–32 · exposure 25 · augmentation 75 · importance 3.6/5 · click for rater detail
Coordinate the look and function of product lines.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While design AI tools (CAD assistants, generative design) are gaining adoption in large firms, strategic product-line coordination remains human-driven; pilots of end-to-end automation are rare, and most organizations treat AI as an assistant to human designers rather than a replacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Design and product development sectors are adopting AI tools for ideation and rendering at a moderate pace, but strategic product-line coordination remains largely a human-led pilot area. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly boost designer productivity by rapidly generating design options, visualizing cross-product consistency, and flagging aesthetic or functional conflicts, allowing human coordinators to focus on strategic trade-offs and stakeholder alignment rather than manual iteration. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools (generative design, mood boards, trend analysis, 3D visualization) meaningfully speed up ideation and consistency checks, helping designers coordinate look and function more efficiently while retaining final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate design variations and analyze product aesthetics, coordinating across a product line requires subjective judgment about brand consistency, market positioning, and cross-functional trade-offs that depend on strategic intent and human stakeholder input. AI cannot currently perform the full coordination task end-to-end with 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires holistic cross-product judgment, brand strategy alignment, and stakeholder coordination that current AI cannot perform end-to-end; AI can support pieces but not replace the coordinating function. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant organizational friction exists: product-line coordination requires sign-off from multiple departments (engineering, marketing, executive), brand governance decisions are legally and strategically sensitive, and design accountability rests with human designers who bear reputational risk. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational trust, brand ownership, and cross-functional decision-making create real friction against full automation of this coordinating role. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI design tools and rendering can reduce some computational work, but the loaded cost of coordination oversight, human creative refinement, and stakeholder management remains comparable to or exceeds the value of automated suggestions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human designers still must do most strategic coordination and stakeholder alignment, so AI tools reduce some labor but don't replace the overall coordination role, keeping costs comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Design generation and rendering tools exist, but no deployed product reliably coordinates entire product-line aesthetics and functionality across organizational constraints and stakeholder preferences. Most commercial use cases require substantial human direction and iteration. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Design tools with AI features exist (generative design, style consistency checkers) but no deployed product autonomously coordinates an entire product line's look and function across teams. |
Develop manufacturing procedures and monitor the manufacture of their designs in a factory to improve operations and product quality.
29CI 25–32 · exposure 20 · augmentation 75 · importance 3.4/5 · click for rater detail
Develop manufacturing procedures and monitor the manufacture of their designs in a factory to improve operations and product quality.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing has piloted AI monitoring and quality control systems, but adoption remains inconsistent and concentrated in large, digitized facilities. Most small and mid-sized manufacturers still rely on human-driven oversight; deep, production-scale AI-led procedure development is not yet widespread. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and industrial design sectors have historically been slower AI adopters compared to information/professional services, with digitization of physical process monitoring still developing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at real-time monitoring, anomaly detection in sensor data, and surfacing improvement suggestions—directly augmenting a designer's or engineer's ability to optimize manufacturing. These tools measurably improve decision-making speed and quality when humans retain control of procedural decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven simulation, predictive maintenance analytics, and generative design tools can meaningfully assist designers in refining manufacturing procedures and flagging quality issues, even though humans remain essential for on-site execution. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with monitoring data (sensor feeds, quality metrics) and suggest procedural improvements via analysis, the task requires real-time factory oversight, judgment calls on process deviations, and iterative design-manufacturing feedback loops that demand human expertise and accountability today. End-to-end automation with 50% time savings at equal quality is not demonstrated. |
| Task automatability | claude-sonnet-5 | 2/5 | This task combines physical factory oversight with hands-on process development that requires on-site judgment, sensor interpretation, and real-time troubleshooting AI cannot yet perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Manufacturing process design carries liability for product defects and safety issues; human engineers are typically legally responsible for procedure sign-off and factory oversight. Regulatory and insurance frameworks expect qualified human decision-makers on the factory floor, creating moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement exists, but liability for manufacturing defects, safety compliance, and the need for physical presence on factory floors creates meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI monitoring and analytics tools carry real infrastructure costs (sensors, platforms, integration), and human designers and manufacturing engineers remain essential for interpretation, decision-making, and accountability. Cost parity or meaningful savings are not yet typical in practice. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While simulation and analytics software can reduce some planning time, the physical monitoring and iterative on-floor problem-solving still require paid human presence, keeping AI's cost advantage limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI systems can monitor manufacturing data and flag anomalies, but no deployed product reliably owns the full cycle of developing procedures and monitoring live factory operations with the judgment and accountability required. Pilot systems and analytics exist, but production-scale autonomous operation of this task is not established. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously develops manufacturing procedures and monitors factory production of a designer's work; this remains a human-led, cross-functional activity with AI only as a peripheral tool. |
Modify and refine designs, using working models, to conform with customer specifications, production limitations, or changes in design trends.
28CI 25–30 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Modify and refine designs, using working models, to conform with customer specifications, production limitations, or changes in design trends.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Design and manufacturing sectors show moderate adoption of AI-assisted tools in pilots and larger firms, but widespread production deployment of autonomous design refinement remains limited; many organizations still favor human-led design processes with tools as secondary aids. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Industrial design is a specialized, moderately digitized field where AI tool adoption is growing but still mostly at pilot/assistive stages rather than deep production-scale automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at rapidly generating design variants, flagging production constraint violations, and suggesting trend-aligned modifications, substantially accelerating a designer's iteration cycle when they remain in the loop to validate and refine choices. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly speeds up generating design variants, visualizations, and simulations, letting designers explore more options and refine faster while retaining final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate design variations and suggest modifications based on parameters, the iterative refinement process demands human judgment on aesthetic trends, customer intent interpretation, and production feasibility trade-offs that current systems cannot fully automate end-to-end. The task requires understanding implicit customer feedback and design context that remains difficult for AI to handle independently. |
| Task automatability | claude-sonnet-5 | 2/5 | Generative design and AI CAD tools can propose modifications, but iterating a working model to meet nuanced client feedback, manufacturing constraints, and trend judgment still requires substantial human decision-making and physical/CAD validation.ed |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Design ownership, intellectual property liability, and client accountability create substantial legal and contractual barriers; customers typically require a human designer's sign-off and responsibility for final specifications, and professional liability for design failures weighs heavily on human judgment rather than algorithmic output. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human, but customer relationships, production feasibility judgment, and liability for manufacturing errors create meaningful organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted design tools have meaningful upfront costs (software, integration, model training) and require significant human oversight to refine outputs, making the total cost-per-task comparable to or higher than a mid-level designer's work for complex modifications. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce some iteration time but still require expert designer oversight, licensing, and integration with CAD/production workflows, so all-in costs remain comparable to or only modestly cheaper than human designers. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Design software with parametric automation and generative features exists, but actual production deployment for complete design refinement remains limited; most tools assist rather than replace the designer. Current AI struggles with the full scope of customer specification translation and production constraint integration at the reliability needed for commercial deployment. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-assisted CAD and generative design tools exist in production (e.g., Autodesk generative design), but they are narrow in scope and rarely handle full iterative refinement against customer specs and production limits autonomously. |
Evaluate feasibility of design ideas, based on factors such as appearance, safety, function, serviceability, budget, production costs/methods, and market characteristics.
28CI 25–30 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Evaluate feasibility of design ideas, based on factors such as appearance, safety, function, serviceability, budget, production costs/methods, and market characteristics.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Design firms are adopting AI-assisted tools (parametric design, cost estimation plugins) at moderate pace, but wholesale replacement of feasibility evaluation remains rare. Most adoption is augmentative (assisting designers) rather than substitutive, and organizational buy-in for AI-led feasibility calls is still nascent. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Industrial design is a specialized, lower-digitization field where AI adoption for holistic judgment tasks remains in early pilot stages rather than deep production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting designers by rapidly evaluating cost data, flagging safety rule violations, performing production-method comparisons, and summarizing market trends. These tools substantially boost designer productivity in information synthesis and iteration, while humans retain judgment on trade-offs and final approval. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by generating cost estimates, simulating manufacturability, analyzing market trends, and providing rapid visual iterations, substantially speeding up the designer's evaluation process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze individual design factors (safety rules, cost data, production methods) in isolation, synthesizing them into holistic feasibility judgments requires contextual trade-off reasoning, aesthetic intuition, and market understanding that current AI systems struggle to perform end-to-end at parity with human designers. Partial automation of cost or safety checks exists, but not the integrated evaluation meeting the ≥50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires synthesizing subjective aesthetic judgment, engineering constraints, market intuition, and business tradeoffs into a holistic evaluation; AI can support pieces but cannot reliably perform the integrated judgment call end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Design feasibility evaluation carries high liability and error costs—poor decisions lead to costly prototypes, safety failures, and market losses. Organizational and client expectations still strongly favor human designers signing off on feasibility; regulatory bodies (especially in safety-critical domains) expect human accountability. These friction points limit substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human, but liability for safety/production decisions and organizational reliance on senior designer judgment create real friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for design analysis are increasingly embedded in CAD software (cloud-based evaluation), reducing per-unit costs, but the human designer's judgment remains expensive and necessary. The cost comparison is still unfavorable for AI autonomy given the low error tolerance in design feasibility decisions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Using AI for partial analysis (e.g., cost modeling, market data synthesis) is cheap, but a full feasibility evaluation still requires expensive human expert time for judgment and sign-off, keeping overall cost comparable to human-driven work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Design software with built-in cost estimators and rule-checking exists, but no deployed product reliably performs comprehensive feasibility evaluation across appearance, safety, function, serviceability, budget, production, and market fit simultaneously. Tools assist with individual dimensions but require substantial human judgment to synthesize into final feasibility calls. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI tools offer cost estimation, simulation, or generative concept feedback, but no deployed product performs comprehensive feasibility evaluation across appearance, safety, function, and market fit reliably in production. |
Investigate product characteristics such as the product's safety and handling qualities, its market appeal, how efficiently it can be produced, and ways of distributing, using, and maintaining it.
28CI 25–30 · exposure 25 · augmentation 75 · importance 3.4/5 · click for rater detail
Investigate product characteristics such as the product's safety and handling qualities, its market appeal, how efficiently it can be produced, and ways of distributing, using, and maintaining it.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While design sectors are digitizing CAD and rendering tools, actual adoption of AI for investigative design work remains limited. Most firms use AI as an auxiliary visualization or research tool rather than replacing investigative design work, and conservative attitudes toward liability slow broader deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Industrial design work involves physical products and iterative prototyping, sectors that have historically been slower to adopt AI compared to purely digital information work, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting this task: summarizing market data, generating production-scenario modeling, organizing distribution/maintenance information, and presenting alternatives—all enabling designers to investigate faster and more comprehensively while maintaining human judgment over final conclusions and safety decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly speed up market research, competitive analysis, and preliminary safety/regulatory literature review, giving designers substantial productivity gains while they retain oversight of physical testing and final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can gather and summarize product characteristics data and generate preliminary analyses of market appeal and production efficiency, but investigating safety and handling qualities requires physical testing, real-world feedback, and judgment that current AI cannot autonomously execute at equal quality. The task demands integrated evaluation across multiple dimensions that only a designer can synthesize meaningfully. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves multi-faceted research spanning safety testing, market analysis, manufacturability, and lifecycle logistics, requiring physical prototyping and human judgment that current AI cannot fully replace end-to-end.However, AI can meaningfully assist with subcomponents like market research synthesis. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Design investigation is a core professional responsibility requiring certified designer judgment, particularly for safety considerations, which often carry liability and regulatory implications. Organizations rely on human designers to sign off on these investigations, and client relationships typically demand human expertise and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no strict licensing requirement governs this specific investigative task, product safety liability concerns and the need for physical validation create meaningful organizational and legal friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for market research and process analysis cost money but likely remain below a senior designer's loaded wage per investigation. However, the comprehensive nature of this task and the need for human oversight and validation still make total cost competitive with or higher than AI alone, especially accounting for setup and quality assurance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with desk research and data synthesis, but the physical testing, safety validation, and hands-on evaluation components still require human labor and equipment, keeping overall costs comparable to human designers. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some tools exist to analyze market trends and production workflows, but no integrated product can reliably investigate all mentioned dimensions (safety, handling, appeal, efficiency, distribution, maintenance) end-to-end. AI performs individual sub-tasks—trend analysis, production modeling—but not the holistic investigative and evaluative process designers perform. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for market research summarization and some design analysis, but no deployed system reliably investigates the full range of safety, handling, manufacturability, and distribution characteristics for physical products. |
Confer with engineering, marketing, production, or sales departments, or with customers, to establish and evaluate design concepts for manufactured products.
25CI 20–30 · exposure 20 · augmentation 75 · importance 4.0/5 · click for rater detail
Confer with engineering, marketing, production, or sales departments, or with customers, to establish and evaluate design concepts for manufactured products.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Design teams are cautiously adopting AI for ideation and iteration support, but actual deployment of AI-led design conferencing and stakeholder alignment remains rare; most adoption remains pilots or assistive use, not replacement of the conferencing role. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Industrial design and manufacturing sectors show slower, more uneven AI adoption compared to information/professional services, with AI mainly used for visualization and drafting rather than stakeholder engagement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly boost designer productivity by pre-drafting concept summaries, flagging engineering constraints, synthesizing feedback from multiple stakeholders, and generating alternative directions—allowing the human designer to focus on strategic judgment and negotiation rather than routine synthesis. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by generating design concepts, visualizations, meeting summaries, and rapid iterations that inform and streamline these cross-functional discussions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in generating design concepts and synthesizing requirements from multiple departments, the task fundamentally requires real-time negotiation, judgment calls on trade-offs between engineering feasibility and market appeal, and human stakeholder alignment. Current AI lacks the contextual depth and decision authority to replace the full conferencing and evaluation loop. |
| Task automatability | claude-sonnet-5 | 2/5 | This task is fundamentally relational and cross-functional, requiring live negotiation, persuasion, and synthesis of diverse stakeholder inputs; AI can support prep and documentation but cannot conduct the actual conferring end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Design decisions directly affect manufacturing feasibility, liability, safety, and market success; organizational culture strongly prefers direct human leadership of cross-departmental design conferences, and professional accountability for design concepts typically rests with licensed or senior design staff. Regulatory and contractual expectations create friction against full delegation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong organizational and interpersonal friction exists since customers and internal stakeholders expect to interact with a human designer who has design authority and accountability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI design assistants reduce labor on concept generation and documentation synthesis, but the integration overhead, human review cycles, and need for domain expertise oversight keep total costs comparable to or higher than the hourly rate of a mid-level designer for this specific conferencing and evaluation task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While transcription/summarization tools are cheap, the actual human judgment, relationship management, and decision-making in these meetings still require paid designer time, so overall cost savings are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can draft design briefs, summarize requirements, and suggest concept variations, but no production system reliably conducts multi-stakeholder design conferences or independently evaluates concept viability against organizational constraints. Deployed solutions remain narrow (e.g., design ideation) rather than end-to-end conferencing. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously runs cross-departmental design conferences or stakeholder negotiations; AI meeting assistants only capture and summarize discussions rather than perform the substantive task. |
Present designs and reports to customers or design committees for approval and discuss need for modification.
25CI 20–30 · exposure 20 · augmentation 75 · importance 4.0/5 · click for rater detail
Present designs and reports to customers or design committees for approval and discuss need for modification.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Design firms are experimenting with AI-assisted presentation creation but remain conservative about automating or removing the designer from client-facing approval discussions; adoption is in the pilot/early tooling phase rather than deep production replacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Design and creative professional services are adopting AI tools for ideation and visualization, but client-facing presentation and approval processes still rely heavily on human interaction with slow uptake of full automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI meaningfully augments this task by rapidly generating presentation decks, formatting reports, visualizing design variations, and drafting talking points, allowing designers to focus on dialogue and persuasion while staying in control of the client interaction. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly enhance presentation quality by generating renderings, mockups, summaries, and anticipating client questions, meaningfully boosting designer productivity while the human still delivers and negotiates. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate presentation materials and draft reports, the core task requires responsive dialogue with stakeholders, real-time persuasion, and negotiation around design modifications—activities that demand human judgment, client relationship management, and the ability to understand nuanced feedback and adapt arguments in the moment. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help generate design visuals and draft talking points, but the live presentation, reading client reactions, negotiating modifications, and building trust with committees requires human presence and judgment that current AI cannot replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: client relationship dynamics favor human-to-human interaction for design approval; liability and accountability for design sign-off typically require the responsible designer to present and defend their work; and organizational norms expect direct designer-stakeholder engagement for approval decisions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but client relationships, trust, accountability for design decisions, and preference for human interaction create meaningful organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-generated presentation materials are cheap, but the marginal cost savings from automating only the report/slide generation phase is modest compared to the designer's loaded wage, since the human must still conduct the live presentation and discussion. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce slides or renderings, but the actual client-facing presentation and negotiation still requires a paid human designer, so overall cost savings on the full task are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI systems can assist with slides and report generation, but no deployed product reliably handles the full interactive presentation and discussion loop, including reading client reactions, addressing objections spontaneously, and negotiating modifications in real time without human presence. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously presents designs to clients and negotiates modifications; this remains a human-led interpersonal activity with AI only as a supporting tool for materials creation. |
Develop industrial standards and regulatory guidelines.
21CI 18–25 · exposure 20 · augmentation 63 · importance 3.8/5 · click for rater detail
Develop industrial standards and regulatory guidelines.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Standards development is inherently slow, institutional, and human-authority dependent. Sectors producing these guidelines (standards bodies, regulatory agencies, professional associations) show minimal AI adoption for this core function. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Standards-setting organizations and regulatory bodies are slow-moving, consensus-driven institutions with limited AI integration into their core deliberative processes so far. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist experts by drafting language, organizing technical evidence, cross-referencing prior standards, and generating comparative analyses. These supports raise productivity in research and documentation phases, though human judgment remains essential to the outcome. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by researching existing standards, summarizing regulations, drafting language, and comparing international guidelines, significantly speeding up preparatory work for human experts. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Developing standards and guidelines requires deep domain expertise, stakeholder consensus-building, and nuanced judgment about safety/compliance trade-offs. AI can assist with research, drafting, and documentation organization, but cannot independently drive the policy judgment or secure institutional buy-in that defines this task. |
| Task automatability | claude-sonnet-5 | 2/5 | Drafting standards involves synthesizing technical knowledge, stakeholder input, legal considerations, and consensus-building across committees, which current AI cannot execute end-to-end reliably. AI can assist with research and drafting but not the full process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Standards bodies, regulators, and industry consortia typically require named human experts and institutional accountability for guideline authorship. Legal liability, professional credentialing, and governance requirements create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Regulatory guideline development typically requires accredited standards bodies, subject matter expert consensus, and legal/regulatory authority sign-off, creating strong institutional and legal barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI research and drafting assistance has modest cost, but the task's core—stakeholder negotiation, expert review, and institutional sign-off—remains human-intensive. Savings are incremental rather than structural, keeping overall cost ratio unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Given the low automatability, AI mainly supplements rather than replaces the human expert and committee work, so cost savings are limited to research/drafting speedups rather than full task cost reduction. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product autonomously develops regulatory standards or industrial guidelines in production. This remains a human-expert and committee-driven process; AI has no established role in generating authoritative standards documentation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously develops industry standards or regulatory guidelines; this remains a human-led, committee-based process with AI only used for research support or document drafting. |
Advise corporations on issues involving corporate image projects or problems.
16CI 11–21 · exposure 5 · augmentation 63 · importance 1.9/5 · click for rater detail
Advise corporations on issues involving corporate image projects or problems.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While design firms explore AI-assisted tools for concept generation, actual adoption of AI for strategic corporate image advisory remains minimal. Clients in this space value human expertise and brand stewardship, limiting velocity of AI substitution. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Design consulting and branding sectors have been slower to adopt AI for strategic advisory functions compared to purely digital, high-volume knowledge work sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist designers by synthesizing market research, generating multiple design directions, or analyzing competitor brand positioning, meaningfully accelerating ideation and analysis phases while the human designer retains strategic control and decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist designers with research synthesis, trend analysis, and generating visual concepts to support their advisory recommendations to corporate clients. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires high-level strategic judgment, stakeholder analysis, and understanding of nuanced corporate dynamics that current AI systems cannot perform end-to-end. While AI can assist with data synthesis or trend analysis, advising on corporate image strategy demands contextual decision-making and accountability that remains firmly in human domain. |
| Task automatability | claude-sonnet-5 | 1/5 | This is high-level strategic advisory work requiring deep client relationship trust, reputational judgment, and contextual business understanding that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Corporate image advice carries high reputational and financial risk; clients expect accountability from named professionals and rarely accept AI-generated strategy recommendations without expert human sign-off. Professional liability and client relationships strongly favor human advisors. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but strong organizational friction and client preference for trusted human advisors with reputational accountability create real adoption resistance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools are not yet cost-effective replacements for strategic corporate advisors. The overhead of human review, refinement, and integration of AI outputs approaches or exceeds the cost of direct expert consultation, especially given liability concerns. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply generate mood boards or draft materials, the actual advisory judgment still requires expensive human expert time, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably advises corporations on image strategy independently. AI tools can generate design concepts or analyze brand perception data, but no production system makes strategic corporate image recommendations without substantial human oversight and validation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product provides autonomous corporate image advisory services to companies; this remains a human consulting engagement with only research-stage AI support tools. |
Supervise assistants' work throughout the design process.
15CI 5–25 · exposure 13 · augmentation 50 · importance 3.3/5 · click for rater detail
Supervise assistants' work throughout the design process.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Design firms remain tradition-bound on supervisory structures; design work is creative and bespoke, and there is no evidence of rapid AI adoption in supervisory roles across the design industry. Organizations continue to rely on experienced humans for mentorship and quality control. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Design and creative industries are adopting AI tools for content generation, but AI-driven supervision of human teams is essentially absent from workplace practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist a supervisor by summarizing work progress, flagging technical issues, or organizing feedback, raising the supervisor's ability to review more work faster; however, final judgment and interpersonal communication must remain with the human. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist supervisors by tracking project status, flagging inconsistencies, or summarizing progress, but the core supervisory judgment and interpersonal guidance remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Supervision requires real-time assessment of work quality, feedback on nuanced design decisions, and interpersonal judgment that AI cannot reliably perform end-to-end. AI tools may flag obvious errors or inconsistencies, but meaningful supervisory oversight demands human expertise and accountability. |
| Task automatability | claude-sonnet-5 | 1/5 | Supervising human assistants requires interpersonal judgment, mentorship, and real-time quality assessment that current AI cannot perform end-to-end; it's inherently a human management function. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory roles carry organizational and legal accountability for work quality and assistant development; removing human judgment from this position faces institutional resistance and raises liability concerns around who is responsible for quality assurance and personnel management. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Supervision involves accountability, mentorship, and organizational authority typically vested in a senior human designer, creating strong structural and professional barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Supervision tools (if they existed) would require integration with design workflows, continuous human oversight to validate AI assessments, and likely wouldn't reduce the need for a human supervisor, making the all-in cost comparable to or higher than retaining the supervisor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory role, so no meaningful cost comparison exists—the human must still be paid for oversight. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production systems reliably supervise creative work with the judgment and accountability required in commercial design. While AI can review technical specs or catch syntax errors, evaluating design quality, providing constructive feedback, and making supervisory decisions remain beyond current deployed products. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages or supervises human design assistants' workflows and creative output; this remains a human managerial responsibility. |
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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.