Graphic Designers

27-1024.00

Median wage $62,960/yr197,830 employed (US)Rank #32 of 923 scored · top 3% by substitution

Design or create graphics to meet specific commercial or promotional needs, such as packaging, displays, or logos. May use a variety of mediums to achieve artistic or decorative effects.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution64

Exposure57

Augmentation86

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

19 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

53%

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%59

panel mean rating 3.4/5 → substitution pressure 59/100

Technical feasibility todayw 20%54

panel mean rating 3.2/5 → substitution pressure 54/100

Cost vs. human wagew 15%67

panel mean rating 3.7/5 → substitution pressure 67/100

Adoption barriersw 20%inverted — strong barriers lower the score80

panel mean rating 1.8/5 (barrier strength) → substitution pressure 80/100

Sector adoption velocityw 10%61

panel mean rating 3.4/5 → substitution pressure 61/100

Task breakdown (19 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.

Maintain archive of images, photos, or previous work products.

CI 77–84 · exposure 75 · augmentation 75 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Design teams and creative agencies already widely adopt cloud-based AI-powered DAM and archival systems (Adobe, Dropbox, Figma integrations) in production, with rapid uptake across digital-first sectors where graphic designers concentrate.
Sector adoption velocity	claude-sonnet-5	3/5	Design and creative agencies increasingly use cloud-based DAM tools, but many small design shops still rely on manual folder systems, so adoption is moderate rather than fast.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI augments designer productivity by auto-tagging, smart search, and intelligent organization, allowing designers to locate assets and previous work quickly rather than manually maintaining archives. This transforms archival usability while keeping human oversight in place.
Augmentation potential	claude-sonnet-5	4/5	AI-assisted search, auto-tagging, and similarity matching significantly speed up a designer's ability to locate and manage archived work, even when humans still curate final organization schemes.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can automate most of the archival workflow today: organizing images by content/metadata, tagging, categorizing, and storing—with minimal human intervention required. However, nuanced decisions about organization schemes or context-specific metadata may benefit from human oversight, preventing a full 5.
Task automatability	claude-sonnet-5	4/5	Digital asset management, tagging, and organization of image archives can largely be automated with AI-powered DAM systems that auto-tag, categorize, and deduplicate files.ed.
Adoption barriers	claude-haiku-4-5-20251001	1/5	No licensing or regulatory barriers prevent archival automation; organizations face no legal requirement for human curation of image archives. Minor friction exists around data governance preferences, but nothing materially blocks substitution.
Adoption barriers	claude-sonnet-5	1/5	No licensing, regulatory, or liability barriers prevent automating file organization and archiving; it's a low-stakes administrative task.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Cloud-based AI archival solutions (S3, Google Photos, Adobe Digital Asset Management) cost pennies per image for storage and indexing, orders of magnitude cheaper than paying a human to manually organize, tag, and maintain image libraries.
Cost vs. human wage	claude-sonnet-5	4/5	Automated tagging, metadata generation, and organization software cost far less per file than manual archival labor, though initial setup and integration add some cost.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products (cloud storage with AI tagging, DAM systems with auto-classification) reliably perform archival tasks at scale in production environments. Minor limitations exist around edge-case categorization or custom taxonomies, but core archival capability is mature and widely used.
Technical feasibility today	claude-sonnet-5	4/5	Mature DAM products (Adobe Bridge, Bynder, Cloudinary) with AI-based auto-tagging and search are already deployed in production at many design organizations.

Draw and print charts, graphs, illustrations, and other artwork, using computer.

CI 75–84 · exposure 75 · augmentation 100 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Graphic design sits in the digital/information economy where AI adoption is rapid; major studios and in-house teams have already integrated generative tools into workflows, and displacement of routine charting/illustration work is measurable and accelerating.
Sector adoption velocity	claude-sonnet-5	4/5	Creative/design and marketing sectors have rapidly integrated generative AI tools into everyday workflows, with widespread production use reported across agencies and in-house teams.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI dramatically augments designer productivity by automating tedious drafts, generating variations, and accelerating iteration cycles, allowing humans to focus on creative direction, brand strategy, and client communication while staying firmly in the loop.
Augmentation potential	claude-sonnet-5	5/5	AI tools dramatically speed up ideation, drafting, and iteration of illustrations and graphics while designers retain control over final creative direction and refinement.
Task automatability	claude-haiku-4-5-20251001	4/5	Current AI generative models (DALL-E, Midjourney, Stable Diffusion) can produce charts, graphs, and illustrations from text prompts with minimal human input, achieving substantial time savings. However, iterative refinement and brand/style consistency checks often require human oversight, preventing a clean 5-rating for full end-to-end automation at equal quality.
Task automatability	claude-sonnet-5	4/5	Generative AI image tools (Midjourney, DALL-E, Adobe Firefly) can produce illustrations and graphic artwork from prompts in seconds, meeting or exceeding the 50% time-saving threshold for many routine design tasks, though final refinement and brand alignment still require human input.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few legal or licensing barriers exist; graphic design automation is unregulated and increasingly expected. The main friction is organizational/cultural (some clients prefer human designers, brand consistency concerns), but nothing legally requires human sign-off.
Adoption barriers	claude-sonnet-5	1/5	No licensing or legal requirement mandates a human create charts or illustrations; adoption is purely a matter of organizational choice and quality preference.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI-generated charts and illustrations cost orders of magnitude less than hiring designers for routine work: a few dollars per image versus hundreds in loaded labor. Printing is similarly automated and cheap.
Cost vs. human wage	claude-sonnet-5	4/5	AI image generation subscriptions cost a fraction of a designer's hourly wage per output, though integration and human review add some cost, keeping it just below the extreme low-cost tier.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products (Adobe Firefly, Canva AI, specialized charting APIs) reliably generate artwork and graphs at scale in production environments. Error rates are low for routine graphics, though complex custom illustrations may require revision, and client-specific refinements are common.
Technical feasibility today	claude-sonnet-5	4/5	Products like Adobe Firefly, Canva AI, and Midjourney are deployed at scale in production design workflows today, though output for precise charts/graphs or brand-specific illustration often requires human correction.

Use computer software to generate new images.

CI 79–79 · exposure 75 · augmentation 100 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Graphic design and marketing sectors show rapid adoption of generative image tools; case studies and industry surveys document widespread deployment in creative workflows since 2022–2023. Adoption is particularly fast in tech, marketing, and online content creation.
Sector adoption velocity	claude-sonnet-5	4/5	Creative/design and marketing sectors have rapidly integrated generative image tools into everyday workflows, with widespread adoption in agencies, marketing teams, and freelance work.
Augmentation potential	claude-haiku-4-5-20251001	5/5	Generative image AI significantly augments designer productivity by rapidly prototyping concepts, generating variations, and handling routine asset creation, allowing humans to focus on art direction, brand coherence, and refinement. This assistive role is already transforming typical design workflows.
Augmentation potential	claude-sonnet-5	5/5	AI image generation dramatically accelerates ideation, concept exploration, and draft creation, letting designers iterate faster while retaining creative direction and final judgment.
Task automatability	claude-haiku-4-5-20251001	4/5	Current generative AI (DALL-E, Midjourney, Stable Diffusion) can produce novel images from text prompts in seconds, often meeting quality thresholds for many use cases. While creative direction and refinement may still require human input, the core image generation task achieves >50% time savings for many graphic design workflows.
Task automatability	claude-sonnet-5	4/5	Generative AI image tools can produce new images from prompts rapidly, meeting or exceeding the 50% time-saving bar for many routine design generation tasks, though refinement and brand-specific polish still often require human input.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Minimal legal barriers exist; generative image tools are widely available and used without licensing requirements. Main friction is organizational (creative teams preferring human artistry) and copyright/ethical concerns around training data, but these do not legally block automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for generating images, though some friction exists around copyright/IP uncertainty, client preferences for human-authored work, and brand consistency concerns.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Cloud-based image generation costs pennies per image at scale (subscription models ~$10–20/month for unlimited use), vastly cheaper than hiring a graphic designer at $25–50+/hour labor cost to generate comparable imagery.
Cost vs. human wage	claude-sonnet-5	5/5	Subscription-based generative image tools cost a few dollars to tens of dollars per month versus hourly designer wages, making per-image generation costs orders of magnitude cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed generative image products are widely used in production by design teams and marketing departments today. Performance is reliable for standard requests, though edge cases and highly specialized or brand-specific outputs still require human oversight or rework.
Technical feasibility today	claude-sonnet-5	4/5	Products like Midjourney, DALL-E, Adobe Firefly, and Stable Diffusion are deployed at scale and widely used in production design workflows today, though outputs still require curation and editing.

Mark up, paste, and assemble final layouts to prepare layouts for printer.

CI 67–89 · exposure 78 · augmentation 75 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Design firms use automation selectively (templates, batch processing, plugin scripts), but uptake remains uneven and often limited to high-volume, low-customization work. Full end-to-end automation remains modest in practice relative to potential.
Sector adoption velocity	claude-sonnet-5	4/5	Graphic design and print production have already deeply digitized and automated this workflow step over the past two decades, with AI-assisted design tools now common in agencies and print shops.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI layout assistance, auto-alignment, and template-driven assembly substantially raise designer productivity by handling repetitive mechanical tasks, freeing attention for creative and strategic decisions while the designer remains active in the loop.
Augmentation potential	claude-sonnet-5	4/5	AI-assisted design and prepress tools significantly speed up layout assembly and error-checking while designers retain creative and final quality control.
Task automatability	claude-haiku-4-5-20251001	4/5	Current design software and AI-assisted layout tools can automate much of the mechanical work of assembly, pasting, and final mark-up with templates and style sheets, achieving significant time savings. However, final quality assurance and creative adjustments often require human oversight, preventing a full 5 rating.
Task automatability	claude-sonnet-5	5/5	This is a well-defined, digital, template-driven task (assembling final print-ready layouts) that modern design software with AI-assisted layout tools can already handle end-to-end with significant time savings.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No licensing requirement mandates human involvement; only organizational workflow friction and designer preference for control present modest barriers. Most design firms retain discretion over automation adoption.
Adoption barriers	claude-sonnet-5	1/5	No licensing, legal, or human-contact requirements apply to preparing print layouts; it's a purely technical production task.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-driven automation (scripting, template-based assembly) costs substantially less than a designer's hourly labor once set up, though initial configuration and oversight add overhead. For high-volume repetitive layouts, the cost ratio becomes strongly favorable.
Cost vs. human wage	claude-sonnet-5	4/5	Automated layout assembly and prepress software drastically cut the time versus manual paste-up, making software-driven workflows far cheaper per unit output than dedicated human labor for this specific step.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Tools like Adobe Creative Suite with scripting, InDesign automation, and some emerging AI layout assistants can perform these tasks in production, but they typically require careful configuration and human verification. Error rates on complex multi-element layouts remain material.
Technical feasibility today	claude-sonnet-5	4/5	Adobe InDesign/Illustrator and automated prepress tools with AI-assisted layout/preflight features are widely deployed in production print workflows today, though some manual QA still occurs.

Determine size and arrangement of illustrative material and copy, and select style and size of type.

CI 70–84 · exposure 70 · augmentation 100 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Graphic design and creative services sectors are rapidly adopting AI layout and type tools, with Canva, Figma, and Adobe all integrating generative capabilities. Marketing, publishing, and in-house design teams are measurably shifting to AI-assisted or AI-first workflows, though high-end branding still lags.
Sector adoption velocity	claude-sonnet-5	4/5	Design and creative software sectors are rapidly embedding generative AI layout features into mainstream tools (Adobe, Canva, Figma), with widespread professional uptake already visible.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI excels at rapidly generating typographic and layout options, enabling designers to iterate, explore variations, and refine at much higher velocity. Designers remain in the loop for final aesthetic and strategic decisions while AI eliminates repetitive layout and sizing work.
Augmentation potential	claude-sonnet-5	5/5	AI tools dramatically speed up ideation and iteration for layout and typography, letting designers explore many options quickly while retaining final creative control.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can automate most of the layout, typographic selection, and arrangement tasks using generative design tools and parametric systems that optimize for visual hierarchy and spacing. However, final creative judgment on stylistic choices and brand alignment typically still requires human oversight, preventing a full 5.
Task automatability	claude-sonnet-5	4/5	AI layout tools and generative design systems can now propose type, sizing, and arrangement of copy and images with strong quality, meeting the time-saving bar for many routine layout tasks, though final refinement often needs human judgment.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No legal or regulatory requirement mandates human design approval; the primary barriers are organizational preference for human creativity and client expectations of bespoke work. These are soft, market-driven friction rather than hard licensing or liability walls.
Adoption barriers	claude-sonnet-5	1/5	No licensing, legal, or regulatory requirement mandates a human perform layout and typography decisions; adoption is purely a matter of quality preference and workflow integration.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI-driven design tools cost a small fraction of a professional designer's hourly rate per layout iteration, and can generate dozens of variations in seconds versus hours of manual work. The cost advantage is substantial and recurring across templates and similar projects.
Cost vs. human wage	claude-sonnet-5	4/5	AI layout generation costs pennies per iteration versus a designer's hourly rate, making it substantially cheaper for producing multiple layout options quickly, though oversight adds some cost.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Multiple deployed products (Canva, Adobe Firefly, Figma's AI features) demonstrably perform automatic layout, type selection, and arrangement at scale with acceptable quality for routine design tasks. Some edge cases and complex briefs still benefit from human review, but the core task is reliably automated in production.
Technical feasibility today	claude-sonnet-5	3/5	Products like Canva Magic Design, Adobe Firefly/Express, and Figma AI plugins auto-generate layouts and typography choices in production, but they still have inconsistent quality and need designer review for brand-specific or complex work.

Prepare digital files for printing.

CI 72–80 · exposure 75 · augmentation 75 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Design and print production industries are digitally mature and actively adopting AI-assisted and automated workflows. Adoption is visible in mid-to-large design studios and print houses, with accelerating pilot programs across the sector.
Sector adoption velocity	claude-sonnet-5	3/5	Design and print production workflows have moderate digitization with common use of automation plugins, but many small design shops still do this manually or semi-manually.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI tools substantially augment designers by automating tedious file-preparation steps, freeing time for creative work. Designers retain quality control and final approval, but their productivity on the overall task increases markedly through AI assistance.
Augmentation potential	claude-sonnet-5	4/5	AI/automation tools substantially speed up file prep, checking, and correction while designers retain final oversight and creative decisions.
Task automatability	claude-haiku-4-5-20251001	4/5	Modern AI tools can handle significant portions of file preparation—color space conversion, format optimization, resolution checking, and asset organizing—with 50%+ time savings. However, final quality assurance and client-specific requirements typically require human oversight, preventing full end-to-end automation.
Task automatability	claude-sonnet-5	4/5	Prepping files for print (color profiles, bleed, resolution checks, package assembly) is largely rule-based and can be handled by scripts, plugins, and AI-assisted preflight tools with human spot-checks, meeting the time-saving threshold in most cases.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Minimal legal or regulatory barriers exist; print-file preparation is not a licensed profession. Some customer preference for human verification and organizational inertia create friction, but nothing prevents rapid AI substitution.
Adoption barriers	claude-sonnet-5	1/5	No licensing, regulatory, or human-contact requirement exists for this technical file-preparation step.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-driven batch processing and automated optimization significantly reduce per-file costs compared to manual preparation labor. Integration into existing design pipelines is now mature, making the cumulative cost per prepared file substantially lower than hiring dedicated print-prep technicians.
Cost vs. human wage	claude-sonnet-5	4/5	Automated preflight and export tools cost a small software subscription fee versus a designer's hourly time, making automated preparation far cheaper for routine jobs.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products (Adobe's automation features, specialized print-prep software, AI-assisted workflow tools) reliably perform many sub-tasks in production environments. Some edge cases and complex custom specifications still require human intervention, but core file-preparation workflows are operationalized.
Technical feasibility today	claude-sonnet-5	4/5	Adobe and print-shop preflight tools (Acrobat preflight, PitStop, Canva/InDesign export automation) reliably automate file preparation for print in production today, though edge cases still need designer review.

Key information into computer equipment to create layouts for client or supervisor.

CI 67–84 · exposure 70 · augmentation 100 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Information-sector adoption is rapid: design firms, agencies, and marketing departments are actively deploying AI layout tools in production. However, some high-end creative studios and luxury brands resist, keeping overall velocity slightly below maximum.
Sector adoption velocity	claude-sonnet-5	3/5	Creative/design and marketing sectors show moderate AI tool adoption, with many firms piloting AI-assisted layout tools but full production reliance still uneven across agencies and freelancers.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI significantly amplifies designer productivity by generating initial layouts, iterating on variations, and handling routine formatting—allowing humans to focus on strategic decisions, brand alignment, and refinement rather than manual keystrokes and templating.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up the mechanical process of inputting content into layouts, letting designers focus on creative decisions while tools handle formatting and population.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can perform layout creation with minimal human input using generative design tools and template-based systems. However, the task requires understanding client preferences and supervisor feedback, which introduces variability that prevents a full 5—current systems still need human oversight for final approval and refinement.
Task automatability	claude-sonnet-5	4/5	Data entry and layout population from specifications is a structured, repetitive task that AI-assisted design tools (templates, generative layout engines) can largely handle, though final client-specific nuance may need review.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or legal barriers exist; design work does not require licensure. However, client relationships and preference for human creative input create moderate organizational friction that slows substitution.
Adoption barriers	claude-sonnet-5	1/5	No licensing, regulatory, or safety barriers prevent automating simple data entry into design software.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI-powered layout generation costs a fraction of hiring a graphic designer, with marginal inference costs and subscription fees amounting to pennies per design versus hourly labor costs of $25–75+ for professional designers.
Cost vs. human wage	claude-sonnet-5	4/5	Once configured, AI-driven layout tools cost a fraction of a designer's hourly wage for routine data entry and template population tasks.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products like Adobe Firefly, Canva's AI features, and design agents can generate layouts from text input at production scale. These tools are widely available and reliable for routine layout tasks, though they occasionally require correction for complex or highly customized designs.
Technical feasibility today	claude-sonnet-5	3/5	Products like Canva, Adobe Express, and generative design plugins exist and are used in production, but they still require human setup and correction for brand-specific or complex layouts.

Research the target audience of projects.

CI 59–92 · exposure 70 · augmentation 100 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Graphic design and creative services are information-rich sectors with rapid AI tool adoption; many agencies already use AI for preliminary research and competitive intelligence analysis.
Sector adoption velocity	claude-sonnet-5	3/5	Marketing and design fields are moderately fast adopters of AI research tools, with pilots and partial integration common in agencies, though full production reliance is not yet universal.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI augments designers by rapidly surfacing research insights, competitor analysis, and demographic findings that designers then synthesize into creative strategy, substantially accelerating the iterative research-to-concept loop.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up gathering and synthesizing audience insights, letting designers quickly generate personas, trend summaries, and competitive analyses while retaining judgment over final creative direction.
Task automatability	claude-haiku-4-5-20251001	5/5	Current AI systems can reliably research target audiences by analyzing demographic data, conducting sentiment analysis, reviewing existing market research, and synthesizing findings from multiple sources—all at a fraction of the time a human would require while maintaining or exceeding quality and comprehensiveness.
Task automatability	claude-sonnet-5	3/5	AI can synthesize demographic, market, and behavioral data into audience profiles quickly, but genuine research (surveys, client interviews, proprietary data access) still requires human-driven effort and validation., so only part of this task meets the time-saving bar.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Minimal barriers exist: no licensing requirement, no legal mandate for human involvement, and while some organizations prefer human validation, customer preference alone does not prevent substitution.
Adoption barriers	claude-sonnet-5	1/5	There are no licensing or regulatory requirements for audience research in design work, and no legal mandate for human sign-off, so adoption faces minimal structural barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI-driven research costs orders of magnitude less than hiring researchers or analysts, requiring only API calls and integration overhead versus billable professional time.
Cost vs. human wage	claude-sonnet-5	4/5	Generating audience research summaries via AI is far cheaper and faster than a designer manually compiling market reports, though some cost remains for verifying and tailoring outputs to specific projects.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed tools (LLMs, data analysis platforms, market research aggregators) routinely perform audience research tasks in production environments; the main limitation is integration with proprietary client data and occasional need for human judgment on nuance rather than research capability itself.
Technical feasibility today	claude-sonnet-5	3/5	Tools like ChatGPT, Perplexity, and market research AI assistants can produce audience summaries and persona drafts today, but they lack access to proprietary client data and can hallucinate market specifics, so adoption is partial and requires human verification.

Create designs, concepts, and sample layouts, based on knowledge of layout principles and esthetic design concepts.

CI 61–84 · exposure 62 · augmentation 100 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Design-adjacent sectors (marketing, digital media, tech, e-commerce) are rapidly adopting generative AI tools in production workflows. Major design platforms have integrated AI; designers are using these tools daily at scale, representing fast, measurable adoption in information-heavy industries.
Sector adoption velocity	claude-sonnet-5	4/5	Creative/design and marketing sectors have rapidly adopted generative AI tools for ideation and layout drafts, with widespread integration into design software workflows.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI excels at augmenting designer productivity: rapid concept generation, variation exploration, mood-board assembly, and layout suggestions keep humans in the loop while dramatically accelerating iteration. Designers report substantial productivity gains when using AI as an assistant rather than replacement.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up ideation and concept generation, letting designers explore more options and iterate faster while retaining creative control and final judgment.
Task automatability	claude-haiku-4-5-20251001	4/5	Current AI systems (DALL-E, Midjourney, Stable Diffusion, Claude with design capabilities) can generate full layout concepts, mockups, and design variations at significant speed. While human refinement is often needed, AI can produce 50%+ time savings on initial ideation, concepts, and sample layouts, meeting the automatability bar.
Task automatability	claude-sonnet-5	3/5	AI image/layout generation tools can produce design concepts and sample layouts quickly, but final professional-quality output typically requires significant human refinement, brand alignment, and iteration.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard barriers exist: design is typically creative/commercial work without regulatory mandates for human sign-off. Adoption is limited mainly by client preference for human designers, organizational inertia, and copyright/IP concerns—soft friction rather than legal blockers.
Adoption barriers	claude-sonnet-5	1/5	No licensing or legal requirement mandates a human graphic designer for concept creation; adoption is limited mainly by quality and taste preferences, not regulation.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	API costs for generative design AI (per design generated) are fractional compared to loaded designer wages ($50–$150/hour). Even accounting for oversight and iteration, AI inference and integration costs are 1–2 orders of magnitude cheaper per design output.
Cost vs. human wage	claude-sonnet-5	4/5	AI generation costs are minuscule compared to designer hourly rates for producing multiple concept iterations, even accounting for review/editing time.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products like generative image AI, design assistants (Adobe Firefly, Canva AI), and multi-modal LLMs reliably produce design concepts and layouts in production. Reliability is high for generating variations; output quality depends on prompt quality but is reproducible at scale in real organizations.
Technical feasibility today	claude-sonnet-5	3/5	Products like Midjourney, Adobe Firefly, and Canva Magic Design are deployed and used in production for concepting and drafts, but reliability for final client-ready layouts is inconsistent.

Write or edit copy for clients.

CI 61–84 · exposure 62 · augmentation 100 · importance 2.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Design and marketing agencies are rapidly integrating LLMs into workflows; many freelancers and in-house teams now use ChatGPT or specialized copywriting tools as standard practice. Adoption is visibly accelerating across digital agencies and tech-forward firms.
Sector adoption velocity	claude-sonnet-5	4/5	Marketing, advertising, and design services are among the fastest-adopting sectors for generative AI text tools, with widespread production use already documented.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI excels at generating initial drafts, exploring alternative headlines and messaging, and editing for tone—all while the designer retains control. This shifts the designer from blank-page composition to rapid iteration and refinement, substantially raising productivity on copy-intensive projects.
Augmentation potential	claude-sonnet-5	5/5	AI is heavily used by designers to draft, brainstorm, and edit copy variations quickly while the human retains final creative and brand judgment.
Task automatability	claude-haiku-4-5-20251001	3/5	AI systems can generate and edit copy drafts with reasonable quality, potentially saving 40–60% of time on routine messaging. However, client-specific nuance, brand voice alignment, and strategic copywriting typically require human refinement, preventing full end-to-end automation at equal quality.
Task automatability	claude-sonnet-5	4/5	LLMs can generate and edit marketing/ad copy quickly, and for many routine copywriting tasks this meets or exceeds the 50% time-saving bar with light human editing.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or legal barriers exist to using AI for copy drafting; client preference for human-written work is the main friction. No licensing requirement or liability shield prevents substitution, though some high-stakes creative accounts may retain human sign-off norms.
Adoption barriers	claude-sonnet-5	1/5	There is no licensing, regulatory, or liability requirement mandating a human write client copy; adoption is purely a business/quality choice.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	API costs for inference are very low (cents per task), and integration is straightforward via web interfaces or plugins. Even accounting for oversight, AI is 5–10× cheaper per word than a copywriter's loaded hourly rate, though final quality control still typically demands human time.
Cost vs. human wage	claude-sonnet-5	5/5	Subscription-based AI copywriting tools cost a few dollars to tens of dollars per month versus hourly designer/copywriter rates, an order-of-magnitude cost reduction per unit of copy produced.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	LLM-based tools (ChatGPT, Claude, Jasper, Copy.ai) are deployed and used for copy generation, but they produce variable quality and often require significant human editing. No mature product reliably replaces human copywriters for high-stakes client work without oversight.
Technical feasibility today	claude-sonnet-5	4/5	Products like ChatGPT, Jasper, and Copy.ai are widely deployed in marketing and design workflows for drafting and editing client-facing copy today.

Develop graphics and layouts for product illustrations, company logos, and Web sites.

CI 55–66 · exposure 50 · augmentation 88 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Adoption is growing in digital-native and mid-market firms (pilots common), but small studios and enterprises with strict brand governance lag. Production displacement remains partial rather than wholesale.
Sector adoption velocity	claude-sonnet-5	4/5	Graphic design and digital media are highly digitized creative-services sectors with rapid uptake of generative AI tools like Canva, Adobe Firefly, and Midjourney already embedded in many workflows.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI tools powerfully augment designer productivity by rapidly generating variations, automating layout composition, and iterating mockups, allowing designers to focus on strategic direction and refinement rather than manual drafting and exploration.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up ideation, mockups, and iteration for designers, serving as a powerful creative aid while humans still guide final concept selection and refinement.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate initial layouts and logo concepts rapidly, but requires substantial human iteration on brand strategy, messaging alignment, and refinement to meet client specifications and quality standards. A designer still performs critical judgment on feasibility, distinctiveness, and strategic fit that AI cannot independently validate.
Task automatability	claude-sonnet-5	3/5	AI image/design generation tools can produce draft graphics, logos, and web layouts quickly, but final client-ready output typically still needs human refinement, brand alignment, and iteration, so full end-to-end automation at equal quality is not yet consistent across all deliverables.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Limited barriers: no legal requirement for a human designer, though brand liability and client relationships create some organizational friction. Creative and aesthetic judgment remain human-preferred, but not legally mandated.
Adoption barriers	claude-sonnet-5	1/5	There are no licensing or legal requirements for who can create graphics, logos, or web layouts, and no regulatory barriers to using AI-generated designs.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI inference is cheap, but integration with design workflows, human review cycles, and oversight to ensure brand compliance and quality add overhead. Total cost per deliverable approaches parity with junior designer wages when all friction is included.
Cost vs. human wage	claude-sonnet-5	4/5	AI tools cost a small fraction of designer hourly rates for generating initial concepts and layouts, though human oversight and revision costs reduce the overall savings somewhat.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Generative AI tools (DALL-E, Midjourney, Adobe Firefly) and layout assistants exist in production, but their outputs require significant human curation, legal review (copyright), and refinement. Error rates in brand consistency, accessibility, and technical requirements remain material for autonomous workflows.
Technical feasibility today	claude-sonnet-5	3/5	Products like Midjourney, DALL-E, Canva AI, and Framer AI are used in production for drafts and templates, but professional logo/web design still commonly involves significant human revision due to inconsistency and lack of brand nuance in AI outputs.

Prepare illustrations or rough sketches of material, discussing them with clients or supervisors and making necessary changes.

CI 55–61 · exposure 50 · augmentation 100 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Graphic design is a digitized, information-sector profession with rapid AI adoption. Many studios now use generative AI for concept sketches and initial drafts in production workflows, representing faster adoption than most sectors.
Sector adoption velocity	claude-sonnet-5	3/5	Design and creative services are moderately fast adopters of generative AI tools for ideation and drafting, though full production reliance remains uneven across firms and client types.
Augmentation potential	claude-haiku-4-5-20251001	5/5	Generative AI is transforming designer productivity by rapidly producing concept sketches, variations, and mockups that designers then refine. This assistive use—human creatives directing and improving AI outputs—is already widespread and highly productive.
Augmentation potential	claude-sonnet-5	5/5	AI significantly speeds up the ideation and rough sketch phase, letting designers generate more concepts faster and iterate rapidly with clients while retaining creative control.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate initial illustrations and rough sketches rapidly, but the iterative client discussion and judgment-based revisions require human oversight. An AI system could produce 50% of drafting work, but the client feedback loop and final approval remain human-dependent.
Task automatability	claude-sonnet-5	3/5	AI image generation tools can produce rough sketches or illustrations quickly, but the iterative client discussion, interpretation of feedback, and revision cycle still require significant human judgment and communication.5"0% of the drafting portion can be automated, but not the full task end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few legal or regulatory barriers exist to automating illustration generation. Client preference for human creativity and designer union/professional norms provide some friction, but these are soft barriers rather than hard licensing or liability requirements.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement blocks AI-assisted sketching, though client relationships and trust in human designers create some organizational friction and preference for human interaction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI image generation costs are now very low (pennies per image), while designer labor costs are substantial ($30-80/hour loaded). Multiple iterations become cheap at scale, though integration and oversight add overhead.
Cost vs. human wage	claude-sonnet-5	3/5	AI image generation is cheap per output, but the human time needed for client meetings, interpreting feedback, and iterative refinement remains substantial, keeping overall cost comparable to traditional methods for the full task.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed products (DALL-E, Midjourney, Stable Diffusion) generate images reliably, but they produce variable quality and often require human refinement. Production use exists in some studios for concept generation, but error rates and quality inconsistency limit full task automation.
Technical feasibility today	claude-sonnet-5	3/5	Tools like Midjourney, DALL-E, and Adobe Firefly are deployed and used by designers to generate rough concepts, but they don't handle client discussion or nuanced revision management autonomously.

Study illustrations and photographs to plan presentation of materials, products, or services.

CI 46–62 · exposure 50 · augmentation 88 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Graphic design is a highly digitized, information-sector field with strong early adoption of AI-powered design tools. Agencies and in-house teams are actively experimenting with AI for asset analysis and layout suggestions, though full end-to-end automation of strategic planning remains limited.
Sector adoption velocity	claude-sonnet-5	3/5	Design and marketing sectors are moderately fast adopters of AI tools for ideation and asset review, though full creative planning workflows still rely heavily on human designers.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI excels at augmenting this task: computer vision can rapidly analyze reference materials, suggest color palettes and layouts, and generate variations. Designers using these tools can plan presentations far faster while maintaining full creative control and strategic direction over the final output.
Augmentation potential	claude-sonnet-5	4/5	AI significantly aids designers by quickly surfacing visual patterns, generating mood boards, and suggesting layouts, meaningfully speeding up the planning phase while the designer retains creative control.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can assist significantly with analyzing visual composition, color theory, and layout principles through computer vision and design automation tools. However, the creative judgment required to plan an effective presentation—understanding brand intent, target audience nuance, and strategic messaging—still requires substantial human direction and refinement to meet production quality standards.
Task automatability	claude-sonnet-5	3/5	AI can quickly analyze visual assets and suggest layout/presentation concepts, but final planning still requires human judgment about brand fit, client intent, and creative direction, so only partial time savings occur end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Client relationships, liability for brand impact, and the need for human creative judgment create moderate friction. Most organizations retain designers in decision-making roles; however, no legal licensing requirement or strict regulatory barrier exists preventing AI augmentation of the planning process.
Adoption barriers	claude-sonnet-5	1/5	No licensing or legal requirement mandates a human for this planning task; adoption is limited mainly by creative quality expectations, not regulation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	While AI tools reduce the cost of generating design options, the planning phase still requires skilled human designers to oversee, critique, and strategically direct the work. The all-in cost (tool subscription + designer oversight) remains comparable to or potentially higher than traditional human designer planning alone.
Cost vs. human wage	claude-sonnet-5	3/5	AI tools reduce time on visual review and ideation but still require designer oversight and integration into workflow, making cost savings moderate rather than order-of-magnitude.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products like AI-powered design tools (Canva, Adobe Firefly) and image analysis systems exist and can suggest layouts or extract design elements from reference images. However, they operate with notable limitations in semantic understanding and creative strategy; they function best as augmentation rather than autonomous executors of this planning task.
Technical feasibility today	claude-sonnet-5	3/5	Tools like Adobe Sensei, Canva AI, and generative design assistants exist and are used in production, but they handle narrow sub-steps (tagging, cropping suggestions, mockups) rather than the full planning process reliably.

Prepare notes and instructions for workers who assemble and prepare final layouts for printing.

CI 50–59 · exposure 45 · augmentation 75 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Graphic design and print production sectors show moderate AI adoption for content generation tasks, but instruction writing remains largely manual; pilots exist but production automation is not yet widespread.
Sector adoption velocity	claude-sonnet-5	3/5	Graphic design and creative/marketing sectors show moderate AI tool adoption (e.g., AI-assisted drafting, image generation) but workflow-specific prepress instruction generation remains a niche, slower-adopted use case.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI-drafted notes and templates significantly accelerate the human designer's instruction-writing workflow, offering real-time suggestions and formatting assistance while the designer maintains quality control and domain judgment.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully speed up drafting of clear, consistent instructions and notes, letting designers focus on refining and validating technical details before handoff to production workers.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate draft notes and basic assembly instructions from design specifications, but requires human review for clarity, accuracy, and context-specific details. The task involves both documentation (automatable) and domain-specific technical knowledge (partial automation only).
Task automatability	claude-sonnet-5	3/5	AI language models can draft clear production notes and instructions given design specs, but require designer input on specific layout details, materials, and edge cases, so it's a partial automation with setup needed to integrate with design files.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or legal barriers exist; the main friction is organizational preference for human-reviewed instructions and liability concerns if automated notes cause production errors or safety issues.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human write these notes, though quality control and coordination with print vendors create some organizational friction favoring human oversight.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference and integration costs are substantially lower than hiring a human designer to write detailed assembly notes, though some oversight labor is still required to ensure accuracy and completeness.
Cost vs. human wage	claude-sonnet-5	3/5	Using an LLM to draft instructions is cheap per-instance, but the overall task still requires human review/integration with layout files, so total cost savings versus a designer's time are moderate rather than order-of-magnitude.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products like GPT-4 and specialized documentation tools can generate initial instructional text, but deployed solutions lack reliable handling of complex visual assembly sequences and print-specific technical requirements without human oversight.
Technical feasibility today	claude-sonnet-5	2/5	While generic text-generation tools can produce written instructions, there are no widely deployed production systems specifically automating designer-to-prepress communication reliably at scale today.

Produce still and animated graphics for on-air and taped portions of television news broadcasts, using electronic video equipment.

CI 46–55 · exposure 50 · augmentation 75 · importance 3.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Mid-sized and larger news organizations are piloting AI-assisted graphics tools, but deployment remains limited. Adoption is faster in digital/web graphics than broadcast television, and cost-sensitive local stations lag major networks in AI integration.
Sector adoption velocity	claude-sonnet-5	3/5	Media and broadcast production is adopting AI-assisted design tools at a moderate pace, with pilots in graphics automation but many stations still relying on traditional human-operated workflows.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI excels at augmenting graphic designers by rapidly generating asset variations, automating layout templates, and handling repetitive animation tasks, allowing designers to focus on creative direction and fact-checking. This assistive mode is already seeing adoption in newsrooms.
Augmentation potential	claude-sonnet-5	4/5	AI substantially speeds up ideation, template generation, and rendering for still and motion graphics, letting designers focus on customization and quality control while the tool handles repetitive production work.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate and edit static graphics and simple animations, and tools like generative models can produce visual assets rapidly. However, the task requires coordination with newsroom timelines, fact-checking integration, and real-time adjustments for on-air broadcasts, which still require human oversight and creative direction to ensure quality and accuracy at production speed.
Task automatability	claude-sonnet-5	3/5	AI tools (e.g., generative video/image models, motion graphics templates) can produce many static and simple animated graphics quickly, but broadcast-quality animated news graphics with precise branding, timing, and integration into live systems still require significant human setup and finishing.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	News broadcasts demand brand consistency, legal accuracy (fact-checking visuals), and editorial accountability. Broadcasters have strong organizational preferences for human accountability in on-air content, and liability concerns around automated graphics slow adoption despite technical capability.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for this creative task, but broadcast standards, brand consistency, and editorial review create moderate organizational friction slowing pure AI substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	While AI tools reduce per-asset creation cost, broadcast graphics require quality assurance, brand compliance, and rapid iteration. The total cost of AI infrastructure, oversight, and human refinement remains competitive with experienced designers rather than substantially cheaper.
Cost vs. human wage	claude-sonnet-5	3/5	AI can cut time on templated or repetitive graphics substantially, but licensing, integration with broadcast systems, and human oversight for quality/brand consistency keep total costs closer to parity rather than order-of-magnitude cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed products (generative image/video tools, motion graphics software with AI assists) exist and can produce usable graphics, but they have material limitations in broadcast-quality output, consistency across segments, and real-time integration with news workflows. Most production still relies on human designers making final decisions.
Technical feasibility today	claude-sonnet-5	3/5	Products like Adobe After Effects with AI plugins, Runway, and automated template systems are used in some newsrooms for simple graphics, but full animated broadcast graphics pipelines still rely heavily on human designers for reliability and polish.

Research new software or design concepts.

CI 32–66 · exposure 38 · augmentation 88 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Design studios and agencies are piloting AI-assisted research and trend-spotting tools, but widespread production deployment remains early; adoption is active but not yet at the velocity seen in pure information sectors.
Sector adoption velocity	claude-sonnet-5	4/5	Creative and design professions have rapidly adopted AI tools (e.g., Adobe Firefly, ChatGPT) for research and ideation, consistent with fast adoption in creative/professional services sectors.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully augment research by rapidly surfacing design trends, competitor work, software features, and inspiration sources; a designer can then curate and synthesize these findings, substantially accelerating the discovery phase while maintaining creative control.
Augmentation potential	claude-sonnet-5	5/5	AI significantly speeds up trend research, competitive analysis, and concept brainstorming, letting designers focus more time on creative execution.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist with researching existing design trends and scanning software features, but cannot independently evaluate conceptual novelty, aesthetic merit, or strategic fit—tasks requiring human judgment and creative synthesis that would need significant oversight to meet a 50% time-saving threshold.
Task automatability	claude-sonnet-5	3/5	AI can summarize trends, search for tools, and synthesize research on design concepts, but curating relevance and applying it to a specific brand/project still requires human judgment.4
Adoption barriers	claude-haiku-4-5-20251001	3/5	No hard legal barrier, but organizational workflows often embed design research as part of collaborative creative process, and designers' judgment on what sources and concepts matter means oversight and review costs remain material.
Adoption barriers	claude-sonnet-5	1/5	No licensing, liability, or regulatory barriers prevent using AI tools for research; it's a low-stakes, exploratory task.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI research tools are becoming cheaper, but integrating them into a designer's workflow plus human review and validation still approaches or matches the loaded cost of a designer spending time on structured research independently.
Cost vs. human wage	claude-sonnet-5	4/5	AI research assistants (chat/search tools) are inexpensive compared to billable designer hours spent manually researching trends and tools.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Tools like AI search agents and trend-analysis systems exist in prototype or limited-scope form, but no mature production system reliably delivers independent research on novel design concepts with the quality a professional designer would require; human curation remains necessary.
Technical feasibility today	claude-sonnet-5	3/5	Chatbots and AI search tools are commonly used today for research tasks like this, but designers still verify and supplement findings manually due to accuracy and currency concerns.

Review final layouts and suggest improvements, as needed.

CI 32–42 · exposure 30 · augmentation 75 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Design tool companies are integrating AI feedback into platforms and some studios experiment with AI critiques, but adoption remains concentrated in larger/more digitally mature firms; most graphic design work still relies on human design review as standard practice.
Sector adoption velocity	claude-sonnet-5	3/5	Design and creative services are moderately fast adopters of AI tools for ideation and drafting, though final review/sign-off remains a human-retained step in most studios.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully assist designers by flagging layout problems (spacing, contrast, alignment), generating alternative suggestions, and accelerating rounds of review, allowing the human designer to focus on strategic refinement and client communication rather than mechanical critique.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist by flagging inconsistencies, suggesting alternative compositions, or generating quick mockup variations, boosting designer efficiency during review.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist with layout critiques (contrast, alignment, readability analysis) but cannot fully replace the subjective aesthetic judgment and client-intent understanding required for comprehensive layout improvement suggestions. The task demands creative and contextual reasoning that current AI struggles with end-to-end.
Task automatability	claude-sonnet-5	2/5	AI can generate design critiques and flag issues like alignment or contrast, but evaluating final layouts for brand fit, client intent, and aesthetic judgment still requires human oversight to reach equal quality at scale.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Client relationships and creative authority create organizational friction—many clients expect human designers to take responsibility for final aesthetic decisions. There is no legal licensing barrier, but professional reputation and accountability norms slow substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but client preference for human aesthetic judgment and accountability for brand-critical final approval creates moderate organizational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for layout feedback have reasonable inference costs but require significant human oversight to validate recommendations; the total cost (tool + quality control) remains comparable to or sometimes exceeds the cost of a designer's direct review.
Cost vs. human wage	claude-sonnet-5	2/5	AI review tools are cheap to run but still require significant human verification and iteration, so all-in cost savings versus a designer's judgment are modest rather than order-of-magnitude.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products exist (Figma's design feedback tools, AI-assisted design critics) that can identify some layout issues and suggest technical improvements, but they operate with material limitations in understanding brand strategy, audience psychology, and novel creative direction—still requiring human judgment to filter suggestions.
Technical feasibility today	claude-sonnet-5	2/5	Some design tools (Adobe Firefly, Canva AI features) offer suggestions and basic critique, but no mature product reliably reviews and improves final layouts as a professional designer would in production workflows.

Photograph layouts, using camera, to make layout prints for supervisors or clients.

CI 25–40 · exposure 20 · augmentation 38 · importance 2.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Graphic design is digitally native but this particular task (physical photography of layouts) remains manual and niche. Adoption of dedicated automation is slow because digital design files already eliminate the need for physical printing in many modern workflows.
Sector adoption velocity	claude-sonnet-5	2/5	This specific task has largely disappeared from practice due to digital design tools rather than being actively automated by AI adoption; the sector adopts AI for design generation, not this legacy photographic step.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could assist by automatically adjusting images for color, contrast, or perspective correction post-capture, and by organizing or tagging captured images. However, the core framing and composition judgment still rests with the human operator.
Augmentation potential	claude-sonnet-5	2/5	AI does not meaningfully assist the physical photographing process itself, though digital mockup and rendering tools have already made this task largely unnecessary in modern workflows.
Task automatability	claude-haiku-4-5-20251001	2/5	Current AI vision systems can capture and process images, but the task requires physical operation of a camera, positioning of layouts, and judgment about framing and lighting that demand human presence and tactile control. The end-to-end workflow from setup to delivery still requires substantial human intervention.
Task automatability	claude-sonnet-5	2/5	This is a physical act of photographing a physical layout, which AI cannot perform, though the downstream need (presenting a visual proof) can be replaced by digital rendering rather than photography.dictionary Modern workflows mostly bypass physical photographing entirely rather than automating it.
Adoption barriers	claude-haiku-4-5-20251001	3/5	No legal licensing is required, but organizational workflows often embed this task as part of a designer's routine, and clients may prefer human-reviewed outputs. Physical constraints (access to layouts, camera positioning) add friction to full automation.
Adoption barriers	claude-sonnet-5	1/5	No licensing, liability, or regulatory barriers exist for this task; it's a simple physical documentation step with no legal protection.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Specialized photography automation equipment is capital-intensive and requires integration with existing workflows. For occasional layout photography, the cost of such systems typically exceeds the wage cost of a designer or photographer spending 30 minutes on the task.
Cost vs. human wage	claude-sonnet-5	2/5	There is no direct AI substitute performing this specific physical photography task, so cost comparison is not meaningful in AI's favor; digital alternatives bypass the need rather than compete on cost.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While automated photography systems exist in specialized contexts (e.g., structured product photography), deploying them reliably for varied layout compositions and angles remains limited. Most graphic design firms still rely on manual photography or digital scanning; no mature off-the-shelf product reliably automates this task at scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs the physical act of photographing a layout; this task is largely obsolete in digital workflows and not a target for AI product development.

Confer with clients to discuss and determine layout design.

CI 25–32 · exposure 20 · augmentation 63 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Design agencies and digital-first companies are actively piloting AI design tools, but human-client consultation remains the norm in production workflows. Adoption is increasing in junior/template-based design roles but slower for high-stakes, bespoke design conversations.
Sector adoption velocity	claude-sonnet-5	2/5
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI augments this task substantially by generating layout options, visualizing alternatives quickly, and reducing iteration cycles. Designers can use AI mockups and suggestions to accelerate client discussions and explore more variations, keeping the human designer as the primary decision-maker and client liaison.
Augmentation potential	claude-sonnet-5	3/5
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can suggest layout designs based on prompts and generate visual mockups, the core task requires interpreting nuanced client preferences, business constraints, and creative vision—elements that demand human judgment and dialogue. Current AI lacks the ability to reliably navigate the iterative client conversation and adaptive decision-making that defines this task.
Task automatability	claude-sonnet-5	2/5	This is a live client discovery/consultation process requiring relationship-building, reading unstated needs, and negotiation, which current AI cannot conduct end-to-end."},"feasibility":{"rating":1,"rationale":"No deployed product autonomously confers with clients to elicit and finalize design requirements; this remains a human-led interaction."},"cost_ratio":{"rating":2,"rationale":"Since AI cannot substitute for the consultative role itself, the comparison is mostly moot except for note-taking or summarization aids, which are cheap but don't replace the core task."},"barriers":{"rating":3,"rationale":"No licensing requirement, but strong client preference for direct human interaction and trust-building creates organizational and relational friction against automation."},"adoption_velocity":{"rating":2,"rationale":"Design and creative services are adopting AI tools for content generation but client-facing consultation remains largely unautomated in practice."},"augmentation":{"rating":3,"rationale":"AI can help designers prepare mood boards, summarize client notes, or generate draft concepts to discuss, improving prep and follow-up around the conversation."}}}, but properly formatted below.import json{
Adoption barriers	claude-haiku-4-5-20251001	3/5	Clients typically expect direct human interaction for creative direction and approval; organizational and customer preference for human contact provides moderate protection. However, no legal or regulatory barrier prevents AI-assisted or AI-driven design proposals from being presented.
Adoption barriers	claude-sonnet-5	3/5
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI design tools are inexpensive, but they cannot replace the consultative labor of a designer conducting a client meeting, interpreting feedback, and iterating on requirements. The human designer's time remains the dominant cost factor in this task.
Cost vs. human wage	claude-sonnet-5	2/5
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Tools exist to generate layout suggestions (e.g., generative design software, AI mockup generators), but no deployed product reliably conducts the full client discussion and requirement-gathering conversation independently. Most systems remain narrow in scope and require human designers to interpret and translate client feedback.
Technical feasibility today	claude-sonnet-5	1/5

Related occupations — Arts, Design, Entertainment, Sports & Media

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.