Floral Designers
27-1023.00Median wage $37,360/yr40,590 employed (US)Rank #454 of 923 scored · top 49% by substitution
Design, cut, and arrange live, dried, or artificial flowers and foliage.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution28
Exposure19
Augmentation41
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
15 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%22
panel mean rating 1.9/5 → substitution pressure 22/100
Technical feasibility todayw 20%14
panel mean rating 1.6/5 → substitution pressure 14/100
Cost vs. human wagew 15%22
panel mean rating 1.9/5 → substitution pressure 22/100
Adoption barriersw 20%inverted — strong barriers lower the score66
panel mean rating 2.3/5 (barrier strength) → substitution pressure 66/100
Sector adoption velocityw 10%12
panel mean rating 1.5/5 → substitution pressure 12/100
Task breakdown (15 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.
Confer with clients regarding price and type of arrangement desired and the date, time, and place of delivery.
57CI 39–76 · exposure 50 · augmentation 63 · importance 4.6/5 · click for rater detail
Confer with clients regarding price and type of arrangement desired and the date, time, and place of delivery.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Florists operate in small, often family-run retail contexts with moderate digitization; while online ordering and chatbots are growing, adoption remains patchy compared to information-sector adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Floral retail is a small-business-dominated, low-digitization sector where AI adoption for client consultation remains nascent and mostly limited to basic online order forms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can assist designers by pre-qualifying leads, capturing detailed client preferences, managing scheduling, and surfacing upsell opportunities, allowing designers to focus on creative work rather than routine intake. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with intake forms, price estimates, and scheduling reminders, freeing designers to focus on creative and personal aspects of the conversation. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems (chatbots, voice agents, scheduling tools) can reliably gather client requirements, discuss arrangement preferences, capture pricing information, and schedule delivery details with minimal human oversight, achieving >50% time savings on information gathering and initial scoping. |
| Task automatability | claude-sonnet-5 | 2/5 | Chatbots or voice AI can capture order details, but negotiating custom arrangement preferences, upselling, and understanding nuanced aesthetic/emotional requests still benefits heavily from human interaction; only partial automation feasible today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Some businesses prefer human warmth and relationship-building in floral sales, and complex custom requests may require human judgment, but no legal or licensing requirement prevents AI from handling initial consultations. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer preference for personal touch in gift-giving and small business norms create some friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference for client conversations, preference capture, and scheduling is negligible in cost compared to paying a floral designer's wage (typically $15–25/hour loaded) to handle routine intake calls. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | A chatbot or scheduling AI is cheap to run per interaction, but integration with POS/delivery systems and handling exceptions still requires human oversight, keeping costs roughly comparable for full service quality. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed conversational AI and chatbot products already perform client consultation, quote generation, and scheduling in e-commerce and service contexts at scale; floral retailers increasingly use automated systems for initial consultations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some florist websites and chat-order systems exist for basic order capture, but few production systems handle full consultative conversation about arrangement style, price negotiation, and logistics reliably. |
Order and purchase flowers and supplies from wholesalers and growers.
54CI 44–65 · exposure 53 · augmentation 63 · importance 4.5/5 · click for rater detail
Order and purchase flowers and supplies from wholesalers and growers.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Floral design is a small, traditionally low-tech sector with limited digitization and slow SaaS adoption. Most floral shops still rely on phone and email ordering with established suppliers, and production-grade AI procurement adoption remains uncommon in this segment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Floral retail is a small-business-dominated, low-digitization sector with limited AI adoption; procurement automation is more common in large retail/distribution but not yet in typical floral shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist by aggregating supplier catalogs, tracking prices and availability in real time, flagging inventory shortages, and generating purchase recommendations—while the designer retains final approval and relationship management with preferred vendors. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can help track inventory levels, forecast demand, compare wholesaler prices, and draft orders, offering real productivity gains while the designer still makes final quality and quantity decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Ordering and purchasing flowers from wholesalers involves primarily structured, repeatable tasks (inventory checking, price comparison, order placement, payment) that current AI systems and agents can handle with minimal manual intervention. Integration with supplier APIs and accounting systems is straightforward, and AI can likely achieve >50% time savings while maintaining quality. |
| Task automatability | claude-sonnet-5 | 3/5 | Ordering flowers and supplies is a structured procurement task (checking inventory, placing orders with known suppliers, tracking prices) that AI/automation tools can substantially handle, though it still requires judgment about seasonal availability, quality, and relationship management with growers. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While wholesalers and growers may prefer direct human relationships, there are no legal or licensing barriers to automated purchasing. The main friction is organizational inertia and supplier preference for human contact, but neither prevents substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human purchaser, but supplier relationships, negotiation, and quality assessment for perishable goods create moderate practical friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automating order placement, price monitoring, and supplier comparison has low marginal cost per transaction once integrated. An AI agent handling routine purchasing would cost significantly less than a human buyer spending time on vendor outreach, order entry, and invoice reconciliation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Small floral businesses would need custom integration with wholesaler ordering systems, and the labor cost of this task is already low relative to the setup/integration cost of an AI ordering agent, making the cost ratio only marginally favorable at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Multiple e-procurement and ERP systems with AI-assisted ordering exist in production, but floral supply chains often involve direct relationships, quality inspection requirements, and variable inventory that create material friction. Products work reliably for standardized ordering but struggle with nuanced supplier negotiations or unusual requests. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | General e-procurement and inventory management software exists, but no widely deployed product specifically automates floral wholesale purchasing end-to-end; most shops still rely on manual ordering via phone, email, or supplier portals with human judgment on quality/freshness. |
Perform office and retail service duties, such as keeping financial records, serving customers, answering telephones, selling giftware items, and receiving payment.
49CI 35–64 · exposure 42 · augmentation 63 · importance 4.2/5 · click for rater detail
Perform office and retail service duties, such as keeping financial records, serving customers, answering telephones, selling giftware items, and receiving payment.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Small retail and service businesses (the majority of floral design firms) have moderate, uneven adoption: larger chains use POS and accounting systems widely, but independent florists often rely on manual processes or basic tools. Broader retail trend shows growing adoption of payment terminals and accounting software, but not yet predominant. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Small independent retail and floral shops are slow adopters of AI tools, lagging behind larger e-commerce and finance sectors in production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted customer service (chatbots answering FAQs, payment reminders, order status), recommendation engines for cross-selling giftware, and financial dashboard summaries all meaningfully reduce a floral designer's administrative burden while keeping them in control of customer relationships and pricing decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can meaningfully assist with bookkeeping, inventory tracking, and even draft responses to customer inquiries, improving efficiency while a human remains in the loop for sales and service. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Financial record-keeping and payment processing can be substantially automated (60–80% time savings with bookkeeping software and integrated payment systems), but customer service interactions and selling giftware items require human judgment, product knowledge, and interpersonal skill. Together these components prevent reaching the full 50% savings threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This bundles physical retail service (in-person customer interaction, payment handling) with digitizable record-keeping; only the latter portion is automatable today, capping overall time savings well below 50%.: |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Most floral design shops operate independently or in small retail chains with minimal regulatory constraints on automating these administrative tasks. Payment processing compliance is standardized and well-supported. The main friction is staff preference to handle relationships and upsell, rather than legal or licensing barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirements, but customer preference for human interaction in small retail settings and the need for someone physically present to handle cash/payment create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Accounting software and payment processing APIs are very low-cost per transaction (typically <$1% of revenue for SaaS), while customer service chatbots for routine inquiries (hours, returns, order status) cost pennies per interaction. A floral shop employee's loaded wage for these back-office tasks is substantially higher. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | For a small shop, integrating AI phone/chat systems and POS automation has setup and maintenance costs that often exceed savings versus a single multitasking employee already on-site. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products reliably automate financial record-keeping (QuickBooks, Xero), payment processing (Stripe, Square), and customer data management in retail settings. Telephone answering and basic customer service routing are also well-established. Only the persuasive selling element remains difficult for deployed systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | POS systems and basic bookkeeping software are mature, but AI-driven customer service and telephone answering for a small floral retail shop remain narrow-scope and unreliable for full task coverage. |
Inform customers about the care, maintenance, and handling of various flowers and foliage, indoor plants, and other items.
43CI 36–50 · exposure 30 · augmentation 63 · importance 3.9/5 · click for rater detail
Inform customers about the care, maintenance, and handling of various flowers and foliage, indoor plants, and other items.
43| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Floral design is a craft-oriented, small-business-dominated sector with moderate digitization. While large online florists have deployed chatbots for basic FAQs, adoption remains inconsistent and pilot-stage in smaller shops. The sector is slower than professional services or finance but faster than purely offline trades. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral retail is a small-business, low-digitization, physical-goods sector with minimal AI agent deployment in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can effectively assist floral designers by generating care summaries, drafting care cards, and suggesting maintenance tips based on flower type and season, freeing designers to focus on arrangement quality and customer rapport. This augmentation is already in use and significantly boosts productivity in information packaging. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help florists quickly look up or draft care instructions for various plants, improving their speed and consistency in ancillary information delivery, but doesn't transform the customer-facing task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate general information about flower care and plant maintenance, the task requires adapting advice to specific customer needs, preferences, and contexts—a nuanced interaction that current systems struggle with consistently. Most of the value lies in personalized guidance rather than rote information delivery, which AI cannot reliably provide at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | An AI chatbot could generate generic plant/flower care instructions, but the in-person, tactile advice tied to a specific purchased arrangement or plant condition requires physical inspection and personalized interaction that current AI cannot fully replicate. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or regulatory requirement mandates human delivery of care instructions, and customers increasingly accept AI-provided guidance in e-commerce contexts. The main friction is customer preference for human expertise and trust, which is surmountable through branding and quality presentation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customers expect personal advice from the seller and small retail shops have limited digital infrastructure, creating moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | An AI system providing care information has minimal per-interaction cost (inference only), whereas a floral designer's time is loaded at $20–40/hour or more. Even accounting for oversight and integration, the marginal cost of AI delivery is substantially lower than human labor for routine information provision. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Generating generic care text is cheap via AI, but integrating it into live customer interactions at a flower shop still requires human staff, so overall cost savings are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Chatbots and FAQ systems deployed by florists can answer basic care questions about common flowers and plants, but they lack the ability to handle edge cases, diagnose specific plant problems from descriptions, or provide tailored advice based on a customer's home conditions. Existing systems work for common queries but fail on nuanced or unusual situations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and care-guide apps exist for general plant care information, but no deployed product reliably substitutes for a florist's real-time, item-specific customer conversation at point of sale. |
Wrap and price completed arrangements.
28CI 24–32 · exposure 16 · augmentation 25 · importance 4.3/5 · click for rater detail
Wrap and price completed arrangements.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floral design is a traditionally low-digitization, small-shop industry with minimal AI adoption. Adoption of automation for physical tasks like wrapping remains negligible in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral retail is a small-business, low-digitization, physically-oriented sector with minimal AI/automation adoption for manual craft and pricing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with pricing suggestions or inventory management linked to wrapped items, but offers minimal augmentation of the core wrapping task itself, which relies on human hands-on judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based point-of-sale and pricing tools can help suggest prices based on cost inputs, offering modest assistance, but the wrapping itself receives no AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Wrapping arrangements requires fine motor control, spatial reasoning, and adaptive handling of delicate flowers—tasks at which current robotics remains poor. Pricing is trivial to automate, but the physical wrapping component dominates the task and cannot be performed reliably by off-the-shelf AI systems. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical wrapping requires manual dexterity and pricing requires contextual judgment (labor, material cost, market rates), so an off-the-shelf AI system cannot perform this end-to-end today.atorial handling remains a bottleneck.rating |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Customer expectation strongly favors human-handled arrangements, and wrapping is visible in the final presentation, creating implicit preference for human touch. However, there is no legal or licensing barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory barriers exist for this task; the constraint is purely physical/robotic manipulation capability, not legal or organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of a robot capable of dextrous wrapping, plus integration and ongoing maintenance, far exceeds the labor cost of a floral designer wrapping arrangements. Current hardware cannot justify the investment for this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic wrapping solutions would be costly to deploy relative to a low-wage floral worker doing this manual task, though pricing software is cheap; overall cost ratio favors humans for the physical component. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs physical flower wrapping reliably today; this remains a manual, human-centric operation. Pricing engines exist but lack integration into typical floral shop workflows for this specific task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical wrapping of floral arrangements; pricing calculators exist but are not integrated into an autonomous end-to-end product for this task. |
Conduct classes or demonstrations, or train other workers.
28CI 20–35 · exposure 20 · augmentation 50 · importance 3.1/5 · click for rater detail
Conduct classes or demonstrations, or train other workers.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Floral design is a creative craft in retail and small-business settings with low overall digitization. Training remains primarily human-driven; only a small share of firms have begun experimenting with recorded content or online modules. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Floral design and retail floristry is a small-scale, physically-oriented trade with low overall AI adoption for instructional delivery. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating instructional videos, lesson plans, or reference images of design techniques, improving the efficiency of trainer preparation. However, the live interaction and hands-on feedback remain human-centered, limiting augmentation upside. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help design curricula, create visual aids, scripts, or answer student questions asynchronously, meaningfully supporting but not replacing the instructor. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Teaching and demonstrating creative techniques require real-time interaction, adaptation to learner questions, and modeling of subjective aesthetic judgment. AI could produce training materials or pre-recorded demonstrations, but cannot dynamically conduct live classes or respond meaningfully to worker feedback at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Teaching floral design involves live physical demonstration, hands-on correction, and interpersonal instruction that current AI cannot perform end-to-end; AI can help create supporting materials but not deliver the class itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Teaching and training workers often require organizational trust, hands-on mentorship, and direct human contact for skill transfer and confidence-building. Many floral design firms rely on experienced workers training apprentices as a core practice, creating both regulatory and cultural friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer/student expectation of hands-on human instruction and physical skill transfer creates moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Developing and maintaining high-quality AI training systems (custom video, chatbot support) is costly; the human trainer's wage for a niche skill like floral design is modest, and the upfront setup burden is substantial relative to direct displacement savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate lesson outlines or videos, but replicating live instruction and hands-on training requires human presence, so all-in cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can generate instructional content and videos, no deployed system reliably conducts live interactive classes or training sessions in floral design. Video generation is improving but cannot match the adaptability, presence, and real-time correction needed in actual training environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts in-person floral design classes or hands-on worker training; this remains a human-delivered service with physical craft demonstration. |
Plan arrangement according to client's requirements, using knowledge of design and properties of materials, or select appropriate standard design pattern.
25CI 18–33 · exposure 20 · augmentation 50 · importance 4.4/5 · click for rater detail
Plan arrangement according to client's requirements, using knowledge of design and properties of materials, or select appropriate standard design pattern.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floral design remains a highly craft-based, small-business sector with low digital infrastructure investment. Adoption of AI tools is minimal; most florists rely on traditional design knowledge and direct customer relationships rather than automated or agent-based solutions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral design is a small-scale, low-digitization craft trade with minimal reported AI adoption in production workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist designers by generating multiple design options, suggesting color schemes, or simulating arrangements before execution, raising productivity in the ideation phase. However, the human designer must still validate, adapt, and physically execute the arrangement, limiting the transformative potential of augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (image generators, mood boards, color-matching apps) can help a florist brainstorm design concepts and visualize arrangements before creating them physically. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate design suggestions and analyze aesthetic principles, the task fundamentally requires understanding client preferences, spatial constraints, material properties, and real-time visual feedback that current systems struggle with reliably. End-to-end automation would require capturing the full iterative dialogue and physical execution, which remains beyond current capabilities. |
| Task automatability | claude-sonnet-5 | 2/5 | Planning a floral arrangement involves physical material knowledge, tactile judgment, and client interaction that current AI cannot execute end-to-end; AI can suggest design concepts but cannot select or handle actual flowers. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: clients expect personalized human consultation and creativity, many high-value events require direct designer-client communication for trust and sign-off, and the final product's visual quality and customer satisfaction depend on human judgment that clients implicitly demand from a professional. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for floral design, but strong customer preference for personalized, human-judged aesthetic choices and physical material handling creates moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI design tools into a floral business workflow still requires skilled human florists to evaluate, modify, and execute recommendations. The overhead of oversight and correction likely exceeds the cost savings from partial automation, keeping total costs near or above human-only labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-generated design suggestions are cheap to produce, but since a human florist must still interpret, adapt, and execute the physical arrangement, the net cost savings are minimal relative to the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools exist for suggesting color palettes and design patterns, but no deployed product reliably translates client requirements into floral arrangements without human refinement. Current systems lack the embodied understanding of material properties (freshness, flexibility, durability) needed for practical execution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product plans and executes floral arrangement designs reliably in production; existing AI tools are limited to generating inspirational images or text descriptions, not usable design plans grounded in real material properties. |
Create and change in-store and window displays, designs, and looks to enhance a shop's image.
23CI 10–35 · exposure 13 · augmentation 50 · importance 3.9/5 · click for rater detail
Create and change in-store and window displays, designs, and looks to enhance a shop's image.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Retail and floristry are moderate-to-low digitization sectors with slower AI adoption. Most floral shops remain small, locally-operated businesses with limited tech infrastructure, and physical retail display work has seen minimal AI agent deployment in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral design and retail merchandising are low-digitization, physically-oriented trades with minimal AI agent deployment in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist designers by generating layout mockups, suggesting color palettes, tracking seasonal trends, and automating administrative scheduling of display changes. These tools meaningfully support human decision-making without replacing the designer's aesthetic judgment and hands-on execution. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (image generation, mood boards, trend inspiration, layout mockups) can help designers plan and visualize displays before physical execution, offering moderate creative assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate design mockups or suggest color schemes and layouts, creating and physically executing actual in-store displays requires spatial reasoning, manual arrangement of real flowers/materials, and subjective aesthetic judgment tailored to physical constraints and brand identity. Current AI cannot reliably handle the full end-to-end task including real-world physical execution and adaptive decision-making. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical arrangement of flowers, fabrics, and props in three-dimensional space with tactile and aesthetic judgment; no AI system can physically construct displays today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements for display creation, shops typically prefer skilled human designers for brand-critical decisions, and the physical, real-time nature of the work creates organizational friction. Customer perception of human creativity in retail displays also creates moderate adoption resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical dexterity, on-site presence, and real-time aesthetic/spatial judgment create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI design tools have upfront and integration costs, but still require a skilled floral designer to execute, curate, and physically arrange. The labor cost of the designer remains dominant, making the all-in cost comparison unfavorable to full automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical task, so AI cost is not comparable; human labor remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Design software and AI tools can assist with visualization and suggestions, but no deployed product reliably performs the complete task of creating and changing physical displays to specified aesthetic standards without human oversight. Production systems exist for design ideation only, not the integrated physical execution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product creates or installs physical in-store floral displays; this remains entirely manual craft work. |
Select flora and foliage for arrangements, working with numerous combinations to synthesize and develop new creations.
19CI 15–24 · exposure 8 · augmentation 50 · importance 4.5/5 · click for rater detail
Select flora and foliage for arrangements, working with numerous combinations to synthesize and develop new creations.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floral design remains a craft-focused, small-business-dominated sector with limited digital infrastructure adoption and slow tech integration; most firms operate locally with minimal automation or agent deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral design is a small-business, physical craft sector with minimal AI adoption or digitization pressure. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can assist by suggesting color combinations, generating reference images, or organizing inventory options, meaningfully speeding up ideation without removing the designer's judgment on final selection and spatial composition. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (image generators, mood boards, inventory/trend suggestions) can help designers brainstorm color and combination ideas, but the physical creative execution remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can suggest color palettes and layout patterns, the task requires iterative creative synthesis and aesthetic judgment across numerous floral combinations. Current systems lack the perceptual depth and real-time feedback loop needed to evaluate how actual stems, textures, and spatial relationships work together in physical arrangements. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical selection and handling of live flowers/foliage, tactile assessment of freshness and texture, and spatial-physical arrangement skill that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Customer preference for human creativity and the bespoke nature of floral design (personalization, occasion-specific sentiment) creates moderate market friction, though no legal licensing requirement formally protects the task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical dexterity, aesthetic judgment, and customer trust in human craftsmanship create moderate practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of meaningful creative synthesis, combined with required human oversight and iteration, exceeds the wages of experienced floral designers who work efficiently through intuition and trained aesthetic judgment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no physical means to perform this task, so there is no viable cost comparison; a human florist remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs this end-to-end task in production. AI can analyze images or generate design mockups, but floral design requires tactile evaluation, understanding of bloom lifecycles, water physics, and spatial arrangement that current systems cannot perform in real operational settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product physically selects and arranges real flora; this remains a manual craft task performed by humans in shops and studios. |
Unpack stock as it comes into the shop.
19CI 15–24 · exposure 8 · augmentation 13 · importance 4.1/5 · click for rater detail
Unpack stock as it comes into the shop.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floral retail is highly fragmented, small-scale, and low-digitization; adoption of robotics for stock unpacking is virtually absent. Laggard sector characteristics dominate. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral retail is a small-business, low-digitization sector with minimal AI or robotics adoption for physical handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI/robotics offer minimal augmentation for unpacking—perhaps vision systems to count/verify items—but do not materially boost human productivity on this manual, routine task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of unpacking boxes and stock in a flower shop. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Unpacking stock is a partly mechanizable task (box opening, removal of items), but requires spatial reasoning, fragility assessment, and placing items in appropriate shop locations—all involving variable environments and physical dexterity that current robots struggle with reliably. No end-to-end automation meets the 50% time-saving threshold in practice. |
| Task automatability | claude-sonnet-5 | 1/5 | Unpacking physical stock requires manual manipulation of boxes, delicate flowers, and materials; current AI has no capability to perform this physical task at all without robotics, which are not deployed for this purpose. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist for automating unpacking. The main friction is physical space constraints in small flower shops and organizational reluctance to invest in specialized robotics for a low-value task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human unpack stock, but the physical dexterity needed with fragile, irregular items creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic unpacking systems, including perception, gripper hardware, integration, and maintenance, remain significantly more expensive than the loaded wage of a retail/warehouse worker performing this manual task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for physically unpacking delicate stock, so any hypothetical automation (robotics) would be far more costly than a human employee doing this simple task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably unpacks arbitrary floral stock (vases, flowers in varied packaging, supplies) end-to-end in real flower shops. Current robotics are research-stage for this unstructured, fragile-goods task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs unpacking floral stock; this is a purely physical, unstructured manual task with no relevant deployed AI or robotic solution. |
Perform general cleaning duties in the store to ensure the shop is clean and tidy.
19CI 10–28 · exposure 8 · augmentation 13 · importance 3.9/5 · click for rater detail
Perform general cleaning duties in the store to ensure the shop is clean and tidy.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Flower shops are typically small, independent retailers with low digitization and minimal automation adoption. Retail cleaning automation is concentrated in large chain warehouses, not specialty boutique florists. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Small retail floral shops are a low-digitization, physical-labor sector with minimal AI/robotics adoption for cleaning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential exists; AI-powered scheduling or inventory tracking to optimize cleaning routes could help marginally, but the core physical task itself offers little opportunity for meaningful AI-assisted productivity gains. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for physical cleaning and tidying tasks in a retail store. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | General store cleaning involves navigating variable physical spaces, handling fragile items, and adapting to dynamic flower displays. Current robots can handle structured environments but struggle with the spatial complexity and object variety typical of flower shops, making end-to-end automation well below the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | General physical cleaning of a retail shop requires manipulation of physical objects and spaces, which current AI systems (software-based) cannot perform; robotic cleaning solutions are not deployed for this specific retail context. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Cleaning is not licensed or heavily regulated, but florists prefer human oversight to avoid damaging inventory, and physical safety in retail requires human judgment. Customer presence and the need for human discretion create moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory barriers exist, but the physical nature of the task and need for a person on-site with other duties creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous cleaning systems, when they exist, are capital-intensive (tens of thousands) and require integration and maintenance. For a small flower shop, the loaded cost of a part-time cleaner remains far cheaper than buying and maintaining specialized robots. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any hypothetical robotic solution would be far more expensive than simply having staff perform the cleaning as part of their duties. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial products reliably perform autonomous general cleaning in unstructured retail environments with flowers and delicate merchandise. While cleaning robots exist for warehouses, deploying them in active floral shops remains research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs general shop cleaning duties in floral retail settings; consumer robotic vacuums exist but do not address tidying, dusting, organizing, or the varied cleaning needs of a flower shop. |
Water plants, and cut, condition, and clean flowers and foliage for storage.
17CI 10–24 · exposure 8 · augmentation 13 · importance 4.4/5 · click for rater detail
Water plants, and cut, condition, and clean flowers and foliage for storage.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floral design remains a craft-oriented, small-business-dominated sector with limited digitization and automation adoption; most florists operate at small scale with minimal capital investment in automation technology, keeping adoption rates low. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral design and retail floristry is a low-digitization, physical craft sector with minimal AI/robotics adoption for hands-on plant handling. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with water scheduling optimization or inventory tracking of prepared materials, but the core manual tasks of cutting and conditioning flowers offer limited augmentation opportunities since current AI cannot reliably interact with the physical materials themselves. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical acts of watering, cutting, and cleaning flowers; software tools do not touch this manual preparation step. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI robotics can perform basic plant watering with fixed irrigation systems, but cutting, conditioning, and cleaning flowers requires dexterous manipulation of delicate botanical materials with variable geometry and fragility that today's robotic systems struggle with reliably. The task involves significant handling of organic materials with quality-sensitive outcomes, exceeding 50% time savings at equal quality for current systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on task involving fine manipulation of delicate perishable materials; no current AI system can perform the actual watering, cutting, or cleaning of flowers. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | This task has moderate barriers: quality and aesthetic standards in floral design create customer preference for human-handled materials, and workplace safety regulations around cutting equipment and chemical handling (conditioning agents) add compliance friction to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but the physical dexterity, perishability of materials, and lack of robotic infrastructure create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of handling delicate flowers are expensive, require significant infrastructure investment, and integration costs would far exceed the loaded wage of a floral designer performing these preparatory tasks, which are typically labor-cost-efficient due to straightforward handling procedures. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI or robotic system offering this service, so the effective AI cost is undefined/infinite relative to a low-wage human florist assistant performing the task manually and cheaply. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform this task end-to-end. While some robotic systems exist in research settings for plant care, production-grade automation for flower cutting, conditioning, and foliage cleaning at quality standards expected in floral design does not exist at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs floral cutting, conditioning, or watering; this remains purely a manual horticultural/craft task with no robotic solution in commercial floral shops. |
Trim material and arrange bouquets, wreaths, terrariums, and other items, using trimmers, shapers, wire, pins, floral tape, foam, and other materials.
15CI 15–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Trim material and arrange bouquets, wreaths, terrariums, and other items, using trimmers, shapers, wire, pins, floral tape, foam, and other materials.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floral design is a small, low-digitization sector dominated by small local businesses and event studios with minimal automation investment to date. Adoption of any AI/robotic solution is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral design is a small-business-dominated, low-digitization physical craft sector showing negligible AI/robotic adoption for the hands-on arranging work itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI could marginally assist with design visualization (generating arrangement mockups from photos or preferences) or ordering/inventory, but cannot materially augment the physical craft of trimming and arranging itself, which remains the core task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design inspiration, color palette suggestions, or customer order management, but offers little direct help with the physical trimming and arranging process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires dexterous manipulation of fragile botanical materials in three-dimensional space, precise cutting, and real-time spatial reasoning—capabilities that current AI robotic systems struggle with in unstructured, natural material environments. Even the most advanced pick-and-place robots cannot reliably perform the fine motor control needed to trim stems, bend wire, and arrange delicate flowers to aesthetic standards. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual craft requiring fine motor dexterity, spatial-aesthetic judgment, and handling of delicate perishable materials—current AI systems cannot perform the physical manipulation involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for floral arrangement itself, customer demand for human artistry and the retail/event context create mild organizational and preference barriers; however, these are soft rather than regulatory hard stops. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the task demands physical dexterity and customer-facing craftsmanship that create strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of a robot capable of manipulating soft, irregular botanical materials plus the engineering overhead for training and maintenance far exceeds the loaded wage of a floral designer, which is relatively modest ($35K–$50K annually in the US). |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task, so any hypothetical automation would require expensive custom robotics far exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product performs end-to-end floral arrangement with the quality, variety handling, and aesthetic judgment required. Robotics research exists (MIT, others), but nothing operates reliably in production floral design studios today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product arranges physical flowers commercially; robotics for delicate, variable organic materials remains research-stage at best. |
Deliver arrangements to customers, or oversee employees responsible for deliveries.
13CI 7–18 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Deliver arrangements to customers, or oversee employees responsible for deliveries.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Floral design is a traditional, locally-rooted service sector with modest digitization. While some florists use third-party delivery services, direct AI displacement of delivery and management roles has not materially advanced. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Floral retail is a small-business-dominated, low-digitization sector where autonomous delivery technology adoption is minimal and slow-moving. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with route optimization for deliveries or scheduling employee shifts, but these are peripheral to the core task of physically delivering arrangements or directly managing staff performance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help with route optimization and delivery scheduling software, offering some efficiency gains, but does not materially change the physical delivery task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Delivery and employee oversight both require physical presence and human judgment. Current AI cannot physically deliver arrangements or manage staff in real-time, making end-to-end automation infeasible today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical delivery of fragile arrangements or supervising delivery staff requires physical presence and human judgment; current AI cannot perform this end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Delivery requires physical presence in customer locations, and employment law mandates human supervision of staff. Customer preference for personal contact during delivery also creates organizational and legal friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical handling of perishable goods and customer-facing delivery interactions create practical barriers to full automation (though drones/autonomous vehicles are emerging). |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous delivery vehicles exist only in limited pilot geographies for simple packages, and employee supervision requires human judgment unavailable from AI. For typical floral delivery contexts, human drivers and managers remain far cheaper than the infrastructure and oversight needed. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no direct substitute for physical delivery labor; any cost comparison favors human drivers or gig-delivery services over hypothetical AI systems. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously deliver physical products or supervise employees. This task inherently requires human presence and decision-making that no production AI platform addresses. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical flower delivery or manages delivery personnel; this remains a human/logistics-driver task, at most aided by routing software. |
Decorate, or supervise the decoration of, buildings, halls, churches, or other facilities for parties, weddings and other occasions.
12CI 5–19 · exposure 8 · augmentation 50 · importance 3.8/5 · click for rater detail
Decorate, or supervise the decoration of, buildings, halls, churches, or other facilities for parties, weddings and other occasions.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Floral design remains a high-touch, artisanal, and geographically distributed service with low digital adoption and fragmented small businesses. The sector has not invested in AI automation; deployment lags far behind information-intensive professions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Floral design and event decoration is a small-business, physically-oriented trade sector with minimal AI/robotics adoption for hands-on installation work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating color and arrangement ideas, showing inspiration galleries, and suggesting material options, helping designers explore possibilities faster. However, the augmentation is partial—human creativity, client consultation, and physical execution remain central. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with design visualization, mood boards, layout planning, and client communication, but does not touch the physical decorating and supervision itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI cannot perform spatial design and physical decoration autonomously. While AI can suggest color palettes and arrangements, the actual execution requires physical manipulation, spatial judgment, and real-time aesthetic decision-making that current systems cannot do end-to-end at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of materials, spatial arrangement in real venues, and hands-on installation that current AI cannot execute; it is fundamentally a physical craft/labor task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong adoption barriers exist: clients typically demand direct human consultation and oversight for major events, venues require licensed or insured professionals for liability, and the personalized, bespoke nature of wedding and event decoration creates customer preference for human expertise and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but physical presence, client trust, aesthetic judgment, and on-site logistics create strong natural barriers to any automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI tools for design suggestion are cheap, but the task fundamentally involves physical labor and real-time site supervision that human florists provide. The total cost of AI + human implementation exceeds hiring a floral designer. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical decorating labor, so the human remains the only cost-effective option; robotics for this niche task doesn't exist commercially. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs physical decoration of spaces autonomously or supervises decoration effectively in production settings. AI design assistants exist but do not replace the human-led supervision and hands-on execution this task requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically decorates venues or supervises on-site installation crews; this remains entirely a human physical and managerial task. |
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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.