Demonstrators and Product Promoters

41-9011.00

Median wage $39,320/yr64,520 employed (US)Rank #286 of 923 scored · top 31% by substitution

Demonstrate merchandise and answer questions for the purpose of creating public interest in buying the product. May sell demonstrated merchandise.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution34

Exposure26

Augmentation50

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

21 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

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%28

panel mean rating 2.1/5 → substitution pressure 28/100

Technical feasibility todayw 20%23

panel mean rating 1.9/5 → substitution pressure 23/100

Cost vs. human wagew 15%27

panel mean rating 2.1/5 → substitution pressure 27/100

Adoption barriersw 20%inverted — strong barriers lower the score66

panel mean rating 2.4/5 (barrier strength) → substitution pressure 66/100

Sector adoption velocityw 10%25

panel mean rating 2.0/5 → substitution pressure 25/100

Task breakdown (21 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.

Record and report demonstration-related information, such as the number of questions asked by the audience or the number of coupons distributed.

CI 61–77 · exposure 70 · 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	Retail and promotional sectors have adopted point-of-sale and coupon-tracking systems for years, but end-to-end autonomous demonstration monitoring (crowd counting, question tracking, reporting) remains in pilot phase rather than mainstream production deployment.
Sector adoption velocity	claude-sonnet-5	2/5	Retail/promotional work is a low-digitization, high-physical-presence sector with minimal AI adoption for this kind of task tracking.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-powered dashboards and real-time analytics augment promoters by providing live feedback on audience engagement and coupon performance, allowing them to adjust messaging mid-demonstration and optimize future events.
Augmentation potential	claude-sonnet-5	4/5	AI tools (e.g., simple apps, voice-to-text, or automated tally systems) can meaningfully speed up recording and compiling this information for the human demonstrator.
Task automatability	claude-haiku-4-5-20251001	4/5	Recording and reporting demonstration metrics (questions asked, coupons distributed) is highly structured data capture that can be automated via computer vision, attendance tracking systems, and coupon redemption logs. Current AI can count objects, track events, and generate reports, achieving substantial time savings with minimal setup.
Task automatability	claude-sonnet-5	4/5	This is a structured data-logging and reporting task—counting simple metrics and compiling them into a report—which is well within the capability of current AI systems given input data or transcripts.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard barriers exist: this is pure data collection and reporting with no licensing requirement or legal sign-off needed. Minor friction may arise from camera/privacy concerns in some venues or organizational preference to maintain human presence for engagement reasons.
Adoption barriers	claude-sonnet-5	1/5	No licensing, regulatory, or liability barriers exist for recording and reporting basic demonstration metrics.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Automated counting (via sensors, cameras, or integrations with existing systems) costs substantially less than paying a human to manually tally and transcribe demonstration data in real time, easily achieving an order of magnitude or more cost advantage.
Cost vs. human wage	claude-sonnet-5	3/5	AI could cheaply process and summarize collected data, but a human still needs to be physically present to count questions and distribute/track coupons, so the overall cost saving is limited to the reporting portion.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products reliably perform count-based tracking and reporting in retail and event contexts—barcode scanners, point-of-sale systems, and CCTV analytics with AI backends operationally track coupon distribution and attendance. Some error margin exists in crowd counting, but the core task is production-ready.
Technical feasibility today	claude-sonnet-5	3/5	While AI can summarize and tabulate data reliably, deployed products for this specific niche (in-person demo tracking) are not common; most such reporting is still done manually via simple forms or spreadsheets rather than AI systems in production.

Prepare or alter presentation contents to target specific audiences.

CI 59–80 · exposure 62 · augmentation 88 · importance 3.9/5 · 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	Marketing, sales, and product teams in digital-forward organizations are rapidly adopting AI tools for content personalization and presentation generation, with widespread pilot and early production deployment across mid-to-large firms.
Sector adoption velocity	claude-sonnet-5	3/5	Marketing and retail promotion sectors are adopting generative AI content tools at a moderate pace, though many demonstrators work in physical retail settings with lower digitization overall.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI significantly augments human promoters and demonstrators by rapidly generating multiple audience-targeted variants, drafting persuasive messaging, and suggesting design improvements, allowing humans to focus on strategy and delivery rather than content production.
Augmentation potential	claude-sonnet-5	4/5	AI significantly speeds up drafting and customizing presentation materials for different audiences, letting promoters focus on delivery and audience engagement.
Task automatability	claude-haiku-4-5-20251001	4/5	Current AI can generate, adapt, and customize presentation content (text, visuals, structure) for different audience segments with high quality and speed, easily exceeding 50% time savings on content creation and modification tasks that previously required manual work.
Task automatability	claude-sonnet-5	3/5	AI can draft and tailor presentation content variants quickly, but final selection, tone calibration for live audiences, and delivery context still require human judgment, limiting full end-to-end automation.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard regulatory or licensing barriers exist; adoption is primarily limited by organizational preference for human creativity, brand-control workflows, and modest oversight costs rather than legal requirements.
Adoption barriers	claude-sonnet-5	1/5	No licensing or regulatory requirement mandates a human create promotional content; adoption is limited only by organizational preference and quality control.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	API costs for AI-driven content generation and adaptation are typically 10–100× lower than paying a human to manually research, write, and design presentations for multiple audience variants.
Cost vs. human wage	claude-sonnet-5	4/5	Generating and adapting presentation content via AI tools is far cheaper than paying a promoter to manually rewrite materials for each audience segment.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products like ChatGPT, Claude, and specialized tools (Beautiful.ai, Gamma.ai) reliably generate and tailor presentation content at scale, though human review for brand consistency and nuance remains standard practice in production workflows.
Technical feasibility today	claude-sonnet-5	3/5	Products like generative slide/content tools (e.g., Gamma, Canva AI, ChatGPT) exist and are used to customize presentation content, but reliability for nuanced audience targeting is inconsistent and requires human review.

Research or investigate products to be presented to prepare for demonstrations.

CI 59–77 · exposure 62 · 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	3/5	Retail and sales sectors show middling AI adoption; research assistance via tools is increasingly common, but end-to-end automation of pre-demonstration research remains inconsistently deployed. Pilots are widespread, but production-scale replacement is not yet the norm in most organizations.
Sector adoption velocity	claude-sonnet-5	3/5	Retail and marketing sectors are moderately adopting AI tools for content and research tasks, but demonstrators/promoters as an occupation are not a fast-adopting knowledge-work sector.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI dramatically augments human demonstrators by rapidly gathering, organizing, and synthesizing product data, competitive intelligence, and specification details, allowing the demonstrator to focus on persuasion, personalization, and live engagement. This is a high-value assistive use case in active deployment.
Augmentation potential	claude-sonnet-5	5/5	AI tools like chatbots and search assistants substantially speed up gathering and organizing product information, letting demonstrators prepare demonstrations far more efficiently while still applying their own presentation skills.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can efficiently research products, compile specifications, identify key features, and summarize competitive positioning using web search, document analysis, and synthesis. This research component comprises the bulk of the task and can achieve >50% time savings at equal quality; a human may still validate final presentation strategy, but the investigation work itself is largely automatable.
Task automatability	claude-sonnet-5	3/5	AI can gather and synthesize product information (specs, reviews, comparisons) quickly, but tailoring this to a live demonstration context and verifying accuracy still requires human judgment., limiting full end-to-end automation.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Minimal legal or regulatory barriers exist; research is not a licensed activity. The main friction is organizational—some companies may prefer human oversight of product knowledge for brand consistency or expertise validation—but no hard legal requirement blocks AI automation here.
Adoption barriers	claude-sonnet-5	1/5	There are no licensing or regulatory requirements for researching product information; it's an unregulated preparatory task.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference and integration cost for product research is a small fraction of a human demonstrator's loaded wage (typically $25–50/hour labor cost), especially at scale. A few dollars per research task compares very favorably to several hours of human research time.
Cost vs. human wage	claude-sonnet-5	4/5	AI-based research (chatbots, search summarization) is very cheap compared to a human spending hours reading manuals and reviews, though some oversight time is still needed.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed AI systems (LLMs with web search, enterprise research tools, product databases) reliably perform product research, competitive analysis, and feature documentation today. Products like ChatGPT with search, Claude, and specialized research tools demonstrate consistent capability in production environments, though some domain-specific investigation may benefit from human verification.
Technical feasibility today	claude-sonnet-5	3/5	Generative AI and search tools are commonly used today for product research and summarization, but demonstrators still need to verify and contextualize findings for in-person presentation, so it's not a fully hands-off product workflow.

Train demonstrators to present a company's products or services.

CI 35–67 · exposure 45 · augmentation 75 · importance 3.7/5 · 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	Retail, e-commerce, and professional services sectors show growing adoption of AI-assisted training platforms and content generation, but production-wide displacement is still emerging; many organizations piloting rather than fully deployed.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and promotional sectors are generally slow adopters of AI-driven training tools compared to knowledge-work sectors, with pilots more common than broad deployment.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can assist human trainers by generating scripts, personalizing learning paths, identifying knowledge gaps in trainees, and providing real-time coaching suggestions, significantly multiplying the productivity of an experienced trainer without removing them from the process.
Augmentation potential	claude-sonnet-5	4/5	AI can generate training scripts, simulate customer interactions, and provide feedback on recorded practice sessions, meaningfully boosting trainer productivity while humans remain in charge of delivery.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can generate training materials, scripts, and presentation frameworks with high quality and consistency; can deliver portions of training via video or interactive modules; and can assess trainee competence through quizzes or simulations, yielding >50% time savings for training content creation and delivery at scale.
Task automatability	claude-sonnet-5	2/5	Training demonstrators involves live coaching, modeling behavior, hands-on feedback, and interpersonal skill-building that AI can support but not fully replace end-to-end today.','rating_note':1},
Adoption barriers	claude-haiku-4-5-20251001	2/5	No legal or licensing requirement mandates human instruction; organizational preference for in-person training and relationship-building between trainer and trainee creates some friction, but no hard barrier prevents AI-driven training adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but companies often prefer human trainers for quality control, brand consistency, and interpersonal coaching, creating moderate organizational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-generated training materials, video synthesis, and automated assessment cost substantially less than instructor time for initial content creation; ongoing personalized coaching still requires human input, but the bulk of repetitive training delivery is now much cheaper than hiring full-time trainers.
Cost vs. human wage	claude-sonnet-5	2/5	Creating training content with AI is cheap, but effective skill-building still requires human trainers or supervised practice sessions, keeping overall costs comparable to traditional training.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	AI-powered learning platforms and LMS systems with AI tutoring exist in production, but reliable end-to-end training (especially for nuanced product pitching and live objection handling) still requires human oversight and iteration; material error rates persist in domain-specific sales techniques.
Technical feasibility today	claude-sonnet-5	2/5	Some e-learning and AI-generated training materials exist, but reliable AI-led interactive coaching of live product demonstration skills is not yet a mature deployed product.

Suggest specific product purchases to meet customers' needs.

CI 35–52 · exposure 30 · 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	4/5	Retail, e-commerce, and consumer-facing sectors are adopting AI-powered recommendation systems rapidly and at scale. Major platforms (Amazon, Shopify, etc.) use these systems in production, and uptake is accelerating in mid-market retail and service environments.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and in-person promotional sectors show slow AI adoption for physical customer-facing sales tasks compared to office-based professional services.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI recommendation tools effectively assist human demonstrators and sales staff by surfacing relevant products, suggesting upsells, and reducing search time for the right match. A human can leverage these suggestions to confirm fit, add context, and close sales more efficiently than working from catalog alone.
Augmentation potential	claude-sonnet-5	3/5	AI can assist promoters via customer data insights, personalized recommendation suggestions, and training materials, improving their pitch even though the core interaction remains human-led.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can filter products by basic attributes and match keywords from customer statements to product features, reliably diagnosing customer needs and recommending purchases requires understanding context, preference nuance, and trade-offs that current systems handle poorly at scale. The task is partially automatable for simple, well-defined product categories with clear customer specifications, but falls short of the 50%-time-saving-at-equal-quality threshold for general-case product recommendation in complex domains.
Task automatability	claude-sonnet-5	2/5	Recommending products in person relies on real-time perception of the customer, physical demonstration, and persuasive social interaction that current AI cannot fully replicate in-store.paid, though chatbot-based recommendation for online contexts is more feasible. In-person promotional roles remain largely human-dependent today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few legal or regulatory barriers prevent automated product recommendation; most jurisdictions do not require a human sign-off. However, organizational and customer friction exist: retailers often value human sales expertise for high-margin items, and customers may distrust algorithmic suggestions, creating moderate adoption friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but customer preference for human interaction and the physical/experiential nature of demonstrations create moderate friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Inference cost for recommendation models is low, and integration into retail platforms is amortized across many users. The all-in cost of AI-driven suggestions per transaction is substantially cheaper than paying a live demonstrator or sales associate for equivalent coverage.
Cost vs. human wage	claude-sonnet-5	2/5	Human promoters are relatively low-wage already, and deploying robotics/AI hardware plus oversight for physical demonstration tasks is not currently cheaper than employing a person.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed recommendation engines (e-commerce and retail platforms) perform product suggestions in production, but with material false-negative rates, poor handling of novel needs, and frequent irrelevance. These systems work reliably only in narrow, data-rich contexts; they struggle with nuanced customer preferences or edge cases that a human demonstrator would catch.
Technical feasibility today	claude-sonnet-5	2/5	Recommendation engines and chatbots exist for online retail, but no deployed product reliably performs in-person, physical product demonstration and tailored suggestion at scale.

Learn about competitors' products or consumers' interests or concerns to answer questions or provide more complete information.

CI 35–45 · exposure 25 · 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	Retail and promotional sectors show middling adoption of AI-driven market research and competitor tracking (many use business intelligence tools), but live product demonstration remains largely human-driven; adoption is uneven across company size and channel.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and product promotion sectors are relatively slow AI adopters for live customer interaction roles compared to information/professional services sectors.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI excels at augmenting human demonstrators by providing instant, comprehensive competitor specs and synthesized customer feedback mid-conversation, allowing the human to answer more thoroughly and confidently without disrupting the sales engagement.
Augmentation potential	claude-sonnet-5	4/5	AI tools can quickly summarize competitor offerings and anticipate consumer questions, meaningfully speeding up a promoter's preparation and knowledge base.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can gather competitor product information via web search and summarize consumer concerns from online reviews, but synthesizing this into nuanced, real-time answers requires understanding context, tone, and genuine consumer pain points that current systems struggle with reliably. End-to-end automation with equal quality would require consistent ability to interpret implicit customer concerns and competitive positioning.
Task automatability	claude-sonnet-5	2/5	The information-gathering component (researching competitor products, common consumer concerns) can be partly automated, but synthesizing this into live, adaptive customer interaction still requires human presence and judgment on the sales floor.
Adoption barriers	claude-haiku-4-5-20251001	2/5	There are no legal or licensing barriers to automating competitive research or consumer feedback analysis, though organizations often prefer human judgment to interpret findings and customer preference for human contact during promotions creates practical friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but the task relies on face-to-face persuasion and trust-building that create organizational and customer-preference friction against full substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Information gathering via AI is extremely cheap—web scraping and LLM queries cost pennies per session—while human demonstrators command $15–25/hour loaded cost; the economics heavily favor automated research infrastructure.
Cost vs. human wage	claude-sonnet-5	2/5	While AI research assistance is cheap, the task is embedded in in-person demonstration work where human wages still dominate total task cost, limiting overall savings.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While AI can scrape and summarize competitor specs and online reviews, no deployed product reliably handles the fluid, conversational aspect of learning about consumers' actual interests and concerns in real-world promotional settings. Chatbots can retrieve facts but often miss the deep listening and adaptive questioning that make this task effective.
Technical feasibility today	claude-sonnet-5	2/5	AI research tools and chatbots can compile competitor data, but no deployed product reliably performs the full task of learning live consumer interests and answering in-person promotional questions at scale.

Identify interested and qualified customers to provide them with additional information.

CI 35–44 · exposure 25 · augmentation 63 · 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	Sales and marketing operations have rapidly adopted AI-powered lead scoring, customer segmentation, and intent detection tools in recent years. Adoption is particularly high in SaaS, finance, and enterprise sales where digitization is mature and ROI is measurable.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and in-person promotional work is a low-digitization, physical-presence sector with slow AI adoption compared to information/professional services.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI significantly augments human demonstrators by surfacing high-probability prospects, scoring lead quality, and personalizing outreach at scale, allowing sales reps to focus conversation time on the most qualified accounts and dramatically improve productivity.
Augmentation potential	claude-sonnet-5	3/5	AI can assist via CRM-integrated lead scoring, customer data lookups, or scripted talking points to help promoters identify and engage prospects more efficiently, though the core in-person judgment remains human-driven.
Task automatability	claude-haiku-4-5-20251001	2/5	Current AI can identify some customer interest signals from digital data and behavioral patterns, but lacks reliable ability to assess true qualification and purchasing intent end-to-end. Real qualification typically requires nuanced judgment about customer needs, budget, and fit that AI struggles with today.
Task automatability	claude-sonnet-5	2/5	Identifying qualified customers involves in-person reading of body language, engagement cues, and live conversation on the floor, which current AI cannot reliably replicate end-to-end in physical retail/demo settings.4wSome digital lead-scoring exists but this task is framed as an in-person demonstrator activity.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Limited barriers exist beyond standard CRM integration and data privacy compliance. There is no licensing requirement, and organizations can freely adopt AI-assisted or automated lead identification with no legal restriction on the automation itself.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but strong customer preference for human interaction in promotional/sales contexts creates moderate organizational and social friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	Automated lead-scoring and CRM tools cost hundreds to thousands monthly, comparable to a junior sales development representative's loaded wage, making the cost roughly equivalent when factoring in integration and oversight requirements.
Cost vs. human wage	claude-sonnet-5	2/5	Replacing the in-person judgment and rapport-building of a human promoter would require sensors, cameras, and interaction systems that are currently costlier and less effective than a trained human demonstrator for this specific task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While lead-scoring and customer segmentation tools exist and are deployed, they operate with material error rates and require significant human validation. No mainstream product reliably performs the full end-to-end task of identifying and pre-qualifying interested customers without substantial human review.
Technical feasibility today	claude-sonnet-5	2/5	Lead-scoring and chatbot qualification products exist for online sales funnels, but no deployed product performs real-time in-person customer qualification during live demonstrations.

Keep areas neat while working and return items to correct locations following demonstrations.

CI 10–66 · exposure 41 · augmentation 13 · 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	2/5	Adoption remains limited to high-automation sectors (large logistics, manufacturing); retail demonstration floors and trade shows—where this task primarily occurs—have slow, tentative adoption of cleanup robots. Most organizations still rely on human staff for these duties.
Sector adoption velocity	claude-sonnet-5	1/5	Retail demonstration and promotional work is a low-digitization, physical-labor sector with minimal AI/robotics adoption for such mundane physical tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI and automation offer minimal augmentation for a human demonstrator or promoter keeping an area neat; the task is largely manual and routine. Organizational apps or inventory-tracking systems could provide marginal assistance in locating item locations, but core cleanup remains low-value for AI enhancement.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance for the physical act of tidying and returning items to shelves or stations during in-person demonstrations.
Task automatability	claude-haiku-4-5-20251001	5/5	Keeping areas neat and returning items to correct locations are physical tasks that current robotic systems can perform with high efficiency and consistency. Mobile manipulation robots and autonomous systems can execute organization and placement tasks faster and more reliably than humans, meeting the ≥50% time-saving threshold.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of objects in real-world retail or event spaces, which current AI systems cannot perform without robotic embodiment far beyond off-the-shelf availability.time saving is negligible today.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Moderate barriers exist: deployment in customer-facing retail environments may face organizational resistance and customer preference for human interaction. However, there are no strict legal or licensing requirements preventing automation of cleanup and organization tasks.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but the physical nature of the task and need for dexterous manipulation in unstructured environments creates practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Capital costs for capable mobile manipulation robots remain substantial ($50k–$250k+) relative to the loaded wage of a demonstrator or product promoter doing cleanup work. Integration, maintenance, and oversight add ongoing costs that make the economics unfavorable compared to human labor for this low-skill task.
Cost vs. human wage	claude-sonnet-5	1/5	Physical robotic manipulation for this task would require expensive specialized hardware and integration far exceeding the cost of a human demonstrator performing simple tidying.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Robotic systems capable of object manipulation and organization exist in production environments (warehouses, logistics), but deploying them for demonstration-area cleanup in diverse retail or trade-show settings faces challenges with variable layouts and item types. Performance is reliable in controlled settings but more limited in unstructured spaces.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical tidying and item replacement in demonstration/retail settings; this remains firmly in the domain of human labor and unaddressed by current robotics products at scale.

Provide product information, using lectures, films, charts, or slide shows.

CI 25–40 · exposure 25 · augmentation 75 · importance 3.8/5 · 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	Sectors using product demonstrators (retail, trade shows, direct sales) tend to be less digitized and more resistant to fully automated presentations; adoption remains largely pilot-stage rather than mainstream production deployment.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and promotional demonstration work is a low-digitization, physical-presence sector where AI adoption for this specific task is minimal and slow.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can significantly assist demonstrators by auto-generating slides, scripts, personalized talking points, and visual assets in real time, substantially raising their productivity and allowing faster iteration on messaging while the human remains the primary performer.
Augmentation potential	claude-sonnet-5	4/5	AI can help create the lectures, slides, scripts, and visual content used in these demonstrations, meaningfully boosting the promoter's preparation efficiency and content quality.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can generate slides, videos, and scripts, end-to-end delivery of product information through engaging demonstrations requires real-time audience interaction, adaptive responses to questions, and live performance elements that current AI systems cannot reliably execute without significant human oversight and setup.
Task automatability	claude-sonnet-5	2/5	Presenting product information in-person to shoppers requires live physical presence, engagement, and adaptive interaction that current AI cannot replicate in a retail/demo setting, though scripted content generation itself could be automated.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	Customer preference for human interaction, brand reputation concerns, and the expectation of live responsiveness create meaningful friction; regulatory requirements vary by product and sector but are generally not absolute blockers to automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but there is organizational and customer preference friction favoring human presence for engagement, persuasion, and answering ad hoc questions.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	While AI can generate presentation assets cheaply, the cost of integrating, maintaining, and overseeing an automated demonstration system—plus the often-irreplaceable value of a live human presenter—makes the all-in cost comparable to or exceeding that of hiring a product demonstrator.
Cost vs. human wage	claude-sonnet-5	2/5	While AI-generated video or slide content is cheap, replacing the live demonstrator role with equivalent audience engagement would require robotics or kiosks that are not cheaper than low-wage promotional staff today.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	AI can create presentation materials and narrate scripts, and some automated demo systems exist, but reliable production deployment of full product demonstrations without human involvement remains limited; most real organizations still rely heavily on human demonstrators for credibility and engagement.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously performs in-person product demonstrations with lectures or slideshows to live audiences at retail venues; this remains a human physical-presence task.

Set up and arrange displays or demonstration areas to attract the attention of prospective customers.

CI 10–52 · exposure 28 · augmentation 38 · 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	2/5	Retail and demonstration roles remain predominantly in lower-digitization, small-to-medium business settings where automation adoption is slow. Few retailers have deployed autonomous display systems at scale.
Sector adoption velocity	claude-sonnet-5	1/5	Retail and promotional work involving physical setup is a low-digitization, physical-labor sector with minimal AI/robotics adoption for this specific task.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI tools can assist by analyzing foot traffic patterns, recommending display layouts, and managing inventory for promotional areas, usefully enhancing a human demonstrator's effectiveness without full replacement.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with planning layouts, generating design mockups, or suggesting product placement strategies, but it does not meaningfully help with the physical execution of setup.
Task automatability	claude-haiku-4-5-20251001	4/5	AI systems with robotics or vision-guided automation could arrange displays and set up promotional areas with minimal human intervention, achieving significant time savings. However, creative optimization for customer attention and real-time adaptation to foot traffic patterns requires some human judgment, preventing a full 5.
Task automatability	claude-sonnet-5	1/5	Physically arranging displays, products, and demonstration areas in a retail or event space requires manual manipulation of physical objects and spatial judgment that current AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Customer preference for human interaction, liability concerns over unattended automated displays, and store layout customization friction create moderate adoption barriers. However, no hard legal requirements prevent automation of the arrangement itself.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but the physical, dexterous nature of the task and need for aesthetic/spatial judgment create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Robotics systems, vision integration, and ongoing oversight are capital-intensive and still more expensive than deploying a human demonstrator for this task. The amortized cost remains above human wage equivalence for typical retail settings.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for physical display setup, so any AI-based approach (e.g., robotics) would be far more costly than a human worker for this task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While computer vision and robotics exist in research and limited industrial settings, reliable production systems for autonomous display setup and arrangement at retail scale remain uncommon. Most deployed solutions require substantial human oversight and manual correction.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously sets up physical retail displays; this remains a hands-on human task with no robotic or AI system in production for this purpose.

Sell products being promoted and keep records of sales.

CI 25–35 · exposure 25 · augmentation 50 · 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	2/5	While CRM systems and sales analytics are widely adopted, autonomous or near-autonomous sales of products at demonstrations remains rare in production. Most sectors still rely on human demonstrators and field sales staff, with AI in supporting rather than replacement roles.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and promotional sales roles are physical, in-person, and low-digitization; AI adoption in this niche is slow compared to office-based sales functions.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by generating customer insights, automating record-keeping, suggesting talking points, and identifying high-intent leads, meaningfully improving human demonstrator productivity on information-heavy aspects of the task.
Augmentation potential	claude-sonnet-5	3/5	AI-powered POS, inventory, and CRM tools help promoters track sales and reorder stock efficiently, meaningfully aiding the administrative side of the task.
Task automatability	claude-haiku-4-5-20251001	2/5	Sales closure requires real-time customer interaction, negotiation, and relationship-building that AI cannot reliably perform end-to-end today. While AI can assist with lead generation and data entry, the human element of persuasion and final sale remain essential, limiting time savings to well under 50% on the full task.
Task automatability	claude-sonnet-5	2/5	In-person selling relies on physical presence, live demonstration, and persuasive human interaction that AI cannot yet replicate on-site; only the recordkeeping portion is automatable.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	High barriers exist because product demonstration and sales typically require human presence, customer trust, and liability for representation of product claims. Regulatory rules around product claims and consumer protection favor human accountability in promotional contexts.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but retailers and brands often prefer human charisma and trust-building for promotions, creating moderate organizational and customer-preference friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI solutions for sales support still require significant human oversight, customer service agents, and integration work, making all-in costs comparable to or exceeding the wage cost of a human demonstrator performing the task.
Cost vs. human wage	claude-sonnet-5	2/5	Automating records is cheap, but the core selling task still requires a paid human demonstrator, so overall cost savings versus a human promoter are limited.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably closes sales autonomously in real-world demonstration or promotional contexts. Record-keeping can be automated, but the core selling component depends on human interaction and remains nascent in production systems.
Technical feasibility today	claude-sonnet-5	2/5	POS and CRM systems reliably automate sales record-keeping, but no deployed product replaces the in-person demonstration/selling function itself.

Recommend product or service improvements to employers.

CI 25–35 · exposure 25 · augmentation 63 · 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	2/5	Retail and demonstration sectors lag in AI adoption; most organizations rely on informal employee suggestions and traditional feedback channels rather than AI-driven improvement pipelines. Pilot programs exist but production deployment remains rare in this workforce segment.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and demonstration roles are low-digitization, physically embedded jobs where AI adoption for feedback synthesis remains nascent and pilot-stage at best.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI tools can help demonstrators organize and prioritize customer feedback and draft improvement suggestions for review, moderately enhancing their ability to synthesize insights and communicate recommendations to management.
Augmentation potential	claude-sonnet-5	4/5	AI can help demonstrators organize, articulate, and structure their observations into clear improvement recommendations, meaningfully boosting the quality and speed of this task while the human remains the source of insight.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can analyze customer feedback and suggest improvements, it cannot independently gather direct customer insights, understand nuanced product contexts, or make judgments about feasibility and competitive value that would meet a 50% time-saving bar for the full task. The core value requires human judgment about organizational priorities.
Task automatability	claude-sonnet-5	2/5	This requires synthesizing firsthand customer interaction observations and tacit experiential feedback into recommendations, which AI cannot originate without human-gathered input; AI can help draft or organize the feedback but not perform the underlying observational task.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Demonstrators hold direct customer relationships and trusted advisor roles; employers prefer recommendations from staff with credibility earned through floor presence and brand familiarity. Organizational friction and customer preference for human-generated insights create substantial adoption barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers, but organizational reliance on human judgment and direct customer contact for credible product feedback creates some friction against pure automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Implementing AI feedback analysis pipelines requires setup and integration costs; the task itself is performed by lower-wage workers whose replacement via AI would need significant infrastructure investment, making the cost comparison roughly comparable or favoring human labor.
Cost vs. human wage	claude-sonnet-5	2/5	AI drafting assistance is cheap, but the valuable input (direct customer observation and interaction) still requires a human on-site, so overall cost savings are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI systems can provide suggestion drafting and feedback summarization, but no deployed product reliably recommends improvements with the credibility and contextual judgment needed for employer adoption. Systems exist for feedback analysis but lack the demonstration floor expertise.
Technical feasibility today	claude-sonnet-5	2/5	There are no deployed products that autonomously generate product improvement recommendations from a demonstrator's floor experience; feedback synthesis tools exist but require substantial human-provided input first.

Practice demonstrations to ensure that they will run smoothly.

CI 18–35 · exposure 13 · augmentation 50 · 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	2/5	Retail and promotional sectors show slow, patchy AI adoption overall. While some use video review tools, systematic AI-driven demonstration practice remains uncommon in production settings.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and in-person promotional work is a low-digitization sector with slow AI adoption for physical rehearsal tasks.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist by providing video analysis, timing feedback, script suggestions, and identifying logical gaps in demonstrations, meaningfully augmenting a human practitioner's preparation without replacing the live practice itself.
Augmentation potential	claude-sonnet-5	3/5	AI could help a demonstrator refine scripts, generate talking points, or simulate audience Q&A for practice, offering moderate assistance short of replacing physical rehearsal.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can help draft or simulate demonstration scripts and identify logical errors, but cannot practice the physical performance, gauge audience reactions, or refine non-verbal communication that are core to smooth execution. Only isolated components (script review, timing checks) can be meaningfully automated.
Task automatability	claude-sonnet-5	1/5	Practicing a live physical demonstration to refine delivery, timing, and audience engagement is an embodied, iterative human rehearsal activity that current AI cannot perform end-to-end.rate
Adoption barriers	claude-haiku-4-5-20251001	2/5	There are minimal regulatory or licensing barriers, but the task fundamentally requires human judgment about live performance quality and audience dynamics, creating organizational and practical friction against full substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing barrier exists, but the physical, in-person nature of live demonstrations and rehearsal creates practical friction against AI substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for demonstration prep (video analysis, script feedback) cost less than dedicated coaching but do not eliminate the need for human practice and in-person feedback, making the cost advantage modest at best.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this physical rehearsal task, so cost comparison favors the human by default since AI provides no equivalent output.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably conducts full demonstration practice with quality feedback on presentation delivery. While video analysis and script-checking tools exist, they lack the contextual feedback and iterative refinement needed for smooth live performance.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product rehearses in-person product demonstrations on behalf of a human demonstrator; this remains entirely a human preparatory activity.

Demonstrate or explain products, methods, or services to persuade customers to purchase products or use services.

CI 16–35 · exposure 13 · augmentation 63 · 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	2/5	Retail and service sectors are adopting chatbots and virtual assistants slowly for initial engagement, but deep replacement of live demonstrators and product promoters is lagging. Most high-touch sales and product demonstration remain human-led in practice.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and promotional sales sectors have low digitization of this specific task and adoption of AI for in-person demonstration remains negligible, though AI chat and video tools are creeping into online sales contexts.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI assists demonstrators effectively by providing instant product information, generating personalized talking points, scripting explanations, and flagging customer objections. A human promoter using AI assistance can cover more ground and adapt faster, making augmentation a strong fit for this task.
Augmentation potential	claude-sonnet-5	3/5	AI can help promoters via scripted talking points, product knowledge lookup, and training simulations, but the live persuasive demonstration itself remains a human-driven, augmented but not transformed activity.
Task automatability	claude-haiku-4-5-20251001	2/5	Current AI systems can generate product descriptions and explanations but cannot replicate the full persuasion task. Live demonstration, reading customer cues, adapting pitches in real-time, and closing sales require human judgment and interpersonal dynamics that AI cannot reliably execute end-to-end with 50% time savings at equal quality.
Task automatability	claude-sonnet-5	1/5	This task requires live, physical in-person demonstration, human presence, and real-time persuasive interaction that current AI cannot replicate end-to-end in the physical retail/trade-show environments where this occurs.dominant.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Mild barriers exist: customers often expect human interaction for high-value or complex products, and liability for misrepresentation or failed persuasion falls on the business. However, no legal requirement mandates human demonstration, so substitution is organizationally possible.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists, but strong customer preference for human interaction, physical presence needs, and employer reliance on charismatic salesmanship create moderate organizational and practical friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI can reduce some support and information-gathering costs, but cannot replace the full persuasion and demonstration role. When factoring in integration, oversight, and the persistent need for human closers, all-in cost remains comparable to or higher than human demonstrators.
Cost vs. human wage	claude-sonnet-5	2/5	While a chatbot could handle some low-value online inquiries cheaply, the physical demonstration component still requires a paid human, so overall cost savings versus the human demonstrator are minimal for the full task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Chatbots and product-description AI exist in production, but deployed systems struggle with real-time persuasion, multi-sensory demonstration, and the contextual adaptation needed to actually move customers to purchase. Most live demonstration and in-person persuasion remains human-driven.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs in-person physical product demonstration and persuasion at retail counters, trade shows, or store aisles; chatbots and video assistants exist but do not substitute for embodied demonstration.

Provide product samples, coupons, informational brochures, or other incentives to persuade people to buy products.

CI 10–35 · exposure 13 · augmentation 38 · 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	2/5	Most promotional sampling occurs in brick-and-mortar retail and events where human presence is still strongly preferred; digitization and automation adoption in this sector remain slow relative to professional services or finance.
Sector adoption velocity	claude-sonnet-5	1/5	Retail demonstration and promotional work is a low-digitization, physical-presence sector with minimal AI agent deployment in this specific function.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist by drafting personalized messaging, identifying high-value prospects, or generating promotional materials; a human demonstrator using such tools could improve efficiency and targeting, though the core persuasion remains human-driven.
Augmentation potential	claude-sonnet-5	2/5	AI could help design coupons, marketing materials, or personalize offers ahead of time, but it offers little real-time assistance during the actual in-person persuasion task.
Task automatability	claude-haiku-4-5-20251001	2/5	Only the informational components (brochure creation, coupon generation) are readily automatable; the core persuasion and sampling distribution require human presence, relationship-building, and real-time judgment that current AI cannot replicate at scale without humans.
Task automatability	claude-sonnet-5	1/5	This task requires physical presence, handing out physical items, and in-person persuasion at a retail location, which current AI systems cannot perform end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	Brand representation, customer preference for human interaction, and the need for real-time contextual judgment create moderate friction; however, no legal licensing or regulatory barrier directly prevents automation of promotional activities.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but the inherently physical, interpersonal nature of handing out items and engaging shoppers creates a structural barrier to remote automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI can assist with material creation at low cost, but the human labor for on-site sampling, face-to-face persuasion, and relationship management remains the dominant cost; AI substitution saves only a fraction of the task.
Cost vs. human wage	claude-sonnet-5	1/5	AI has no viable substitute for physical distribution of samples and face-to-face persuasion, so there is no comparable AI cost basis; the human is the only option.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While AI can generate marketing materials and identify target audiences, no deployed product reliably performs the end-to-end task of physically distributing samples and persuading consumers in real-world retail or event settings without human involvement.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical in-store demonstrations or hands out samples/coupons to shoppers; this remains a human, physical-world task.

Instruct customers in alteration of products.

CI 13–30 · exposure 13 · augmentation 38 · importance 3.9/5 · 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	Retail and product-demonstration sectors have adopted digital tools slowly compared to information and finance sectors; most alteration instruction remains in-person and human-driven, with AI adoption limited to supplementary video or chat support.
Sector adoption velocity	claude-sonnet-5	2/5	Retail and product demonstration roles are physical, customer-facing, and low-digitization sectors with slow AI adoption for hands-on tasks.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist by generating instructional videos, pre-drafting alteration guides, or providing real-time product specs to demonstrators, improving their productivity and consistency. However, the human demonstrator remains central to real-time customer interaction and problem-solving.
Augmentation potential	claude-sonnet-5	2/5	AI could provide reference materials, video guides, or chat-based tips beforehand, but offers minimal real-time assistance during the actual physical instruction interaction.
Task automatability	claude-haiku-4-5-20251001	2/5	Teaching product alterations requires real-time responsiveness to customer questions, demonstration of physical techniques, and adaptive explanation—skills current AI struggles with reliably. While AI could draft instructional content, the live, interactive, and tactile nature of product alteration instruction resists end-to-end automation at the ≥50% time-saving threshold.
Task automatability	claude-sonnet-5	1/5	This requires physical, hands-on demonstration and personalized fitting/alteration instruction that current AI cannot perform in-person; no end-to-end automation is feasible today.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Customers typically expect human interaction and expert judgment during product alteration instruction, and many alterations involve liability concerns (fit, safety, fabric damage). Organizational norms favor human demonstrators, though no hard legal mandate strictly requires it.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement exists, but the inherently physical, hands-on nature of instructing alterations and customer preference for human interaction create moderate practical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI systems (chatbots, video generation, agents) combined with integration and oversight costs remain comparable to or exceed the loaded wage of entry-level demonstrators, especially when accounting for the need for human backup and quality control.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot substitute for the physical task, so any attempted solution (e.g., video tutorials plus human staff) costs more than simply having a human demonstrator, making AI not cost-competitive.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs live customer instruction on product alterations at scale. Chatbots and video tutorials exist but cannot match the responsiveness, error-correction, and personalization of human demonstrators, and they fail on physical/embodied aspects.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical alteration instruction to customers; this remains firmly in the physical, in-person domain unaddressed by current AI products.

Transport, assemble, and disassemble materials used in presentations.

CI 15–24 · exposure 8 · augmentation 25 · 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	1/5	Demonstrator and product promotion roles are typically small-scale, low-tech, and located in varied venues with ad-hoc requirements. The sectors employing these roles (events, retail, small conferences) show minimal adoption of automation technology and remain labor-intensive and distributed.
Sector adoption velocity	claude-sonnet-5	1/5	Product demonstration and promotional work is a physical, low-digitization occupation with minimal AI/robotics adoption for material handling tasks in production settings.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with some planning tasks (optimizing material layout or assembly sequences via computer vision or scheduling), but the core physical execution leaves limited room for meaningful human–AI augmentation. The task is inherently hands-on and does not benefit substantially from AI assistance while the human remains in the loop.
Augmentation potential	claude-sonnet-5	2/5	AI could help with planning logistics or checklists for setup, but it offers little direct assistance with the physical transport and assembly work itself.
Task automatability	claude-haiku-4-5-20251001	2/5	Physical transportation, assembly, and disassembly of presentation materials involves diverse, unstructured objects in varied environments. Current robots struggle with dexterous manipulation, spatial reasoning in dynamic settings, and adaptation to novel material configurations—tasks that require human-level object recognition and fine motor control. Only narrow, pre-programmed logistics scenarios would meet the 50% time-saving bar.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring transporting and manually assembling/disassembling display materials, which current AI systems cannot perform without embodied robotics that are not generally deployed for this purpose.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few licensing or regulatory barriers apply to automating this purely physical task; customer preference and organizational inertia provide only modest friction. The main barrier is technical immaturity rather than legal or liability constraints, meaning automation risk is low today even where technically feasible.
Adoption barriers	claude-sonnet-5	2/5	No licensing or regulatory requirement mandates a human specifically, but physical dexterity, mobility, and real-world manipulation create a practical barrier to any automated substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotics systems capable of general material handling, assembly, and disassembly remain prohibitively expensive—often $50k–$500k+ in capital plus integration—compared to paying a minimum-wage demonstrator $15–$20/hour for the same labor. Even accounting for volume, the per-task cost strongly favors human workers.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-based substitute for this physical labor task, so any hypothetical automation (e.g., general-purpose robotics) would be far more expensive than a human worker today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI or robotic product reliably performs general physical material transport, assembly, and disassembly as described. While specialized warehouse robots exist for narrow cases, they do not handle arbitrary presentation materials with the flexibility this task demands. Production systems capable of this remain research-stage.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical transport and assembly of presentation materials; this remains firmly in the domain of human labor or at best specialized industrial robots not used in this context.

Stock shelves with products.

CI 15–24 · exposure 8 · augmentation 13 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Retail and logistics sectors show minimal production adoption of autonomous shelf-stocking; pilots remain rare and confined to niche environments. Most displacement is not yet happening in general retail environments.
Sector adoption velocity	claude-sonnet-5	1/5	Retail and promotional work is a physically-oriented, low-digitization sector where robotic automation adoption for shelf-stocking remains rare and experimental.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with inventory optimization and route planning for human stockers, but the physical execution remains human-dependent, limiting the productivity lift to logistical planning rather than task execution itself.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer little direct assistance for the physical act of placing products on shelves, though inventory software may indirectly inform stocking decisions.
Task automatability	claude-haiku-4-5-20251001	2/5	While some inventory management and shelf-placement logistics could be optimized by AI, the physical manipulation of products, navigation of varied store layouts, handling of fragile items, and real-time adjustment for shelf space requires embodied robotics that is not yet reliable or cost-effective at scale in general retail environments. Current AI cannot consistently perform the full task end-to-end.
Task automatability	claude-sonnet-5	1/5	Physical shelf-stocking requires manipulation of real objects in a store environment, which current general-purpose AI cannot perform; this is a robotics/embodied task not addressed by software AI.true automation would require dedicated robotic hardware, not yet widespread.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing barriers, retailers face significant organizational friction around liability for product damage, customer interaction preferences (promoters serve dual roles), and the capital and integration risk of deploying untested automation in high-volume, variable environments.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human stock shelves, but physical store environments, liability for damage/safety, and lack of robotic infrastructure create moderate practical friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current robotic shelf-stocking systems (hardware + integration + oversight) are substantially more expensive than hiring a human demonstrator or product promoter, making them economically unviable for most retailers at present.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic shelf-stocking systems are costly to purchase, install, and maintain compared to low-wage human labor performing this task, making AI/robotic substitution currently more expensive.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products reliably perform general shelf-stocking in production retail environments. Experimental robotic arms exist in controlled settings, but they do not operate autonomously across diverse product types, shelf configurations, and store layouts as they exist today.
Technical feasibility today	claude-sonnet-5	1/5	No mature deployed product autonomously stocks shelves with the range of products and store layouts demonstrators/promoters handle; retail robotics pilots exist but are narrow and not integrated into this role's workflow.

Visit trade shows, stores, community organizations, or other venues to demonstrate products or services or to answer questions from potential customers.

CI 5–19 · exposure 8 · augmentation 25 · 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	1/5	This task remains almost entirely dependent on human labor across retail, trade shows, and community venues. Adoption of autonomous systems is negligible; the sector is composed largely of small firms and traditional retail with low digitization.
Sector adoption velocity	claude-sonnet-5	1/5	Retail demonstration and promotional work is a low-digitization, physical-presence sector with minimal AI agent deployment for in-person customer engagement.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist marginally by preparing demonstration scripts, product facts, or FAQ responses ahead of time, but it offers limited real-time assistance during live customer interaction. The core task—engaging people face-to-face—remains primarily human-dependent.
Augmentation potential	claude-sonnet-5	2/5	AI can help promoters prepare talking points, product info, or scripts beforehand, but offers little real-time assistance during actual in-person demonstrations.
Task automatability	claude-haiku-4-5-20251001	2/5	The task requires physical presence at venues and real-time interaction with potential customers to demonstrate products and answer questions. While AI could assist with script generation or FAQs, current systems cannot physically travel to venues or engage in dynamic, context-dependent live interactions that meet the ≥50% time-saving threshold.
Task automatability	claude-sonnet-5	1/5	This task requires physically traveling to venues, setting up demonstrations, and engaging in real-time in-person interaction with the public, none of which current AI can perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: the task requires physical human presence and direct customer interaction, which many brands and organizations prefer for authenticity and relationship-building. Regulatory and liability concerns around autonomous systems in public spaces further restrict deployment.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement exists, but strong customer preference for face-to-face interaction, physical presence needs, and employer reliance on human charisma create meaningful friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI systems capable of any meaningful automation of this task would require significant hardware (robots), integration, and oversight costs that far exceed the loaded wage of a demonstrator, making substitution economically infeasible.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute performing this physical, mobile, in-person task, so cost comparison favors the human by default since AI cannot deliver the output at all.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product today can autonomously visit venues, set up demonstrations, and conduct real-time customer conversations. This task fundamentally requires embodied presence and nuanced interpersonal engagement that is research-stage at best.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product physically visits venues and demonstrates products to walk-up customers; this remains entirely a human physical-presence task.

Wear costumes or sign boards and walk in public to promote merchandise, services, or events.

CI 5–15 · exposure 0 · augmentation 0 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	This task remains in low-digitization, physical sectors with minimal AI adoption. Traditional human demonstrators remain the standard practice in retail and event promotion.
Sector adoption velocity	claude-sonnet-5	1/5	Promotional/marketing gig work involving physical presence is a low-digitization sector with essentially no AI/robotic displacement occurring.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance to a human wearing a costume and walking to promote merchandise; the task is fundamentally human performance-based with no significant augmentative AI application.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance to the physical act of wearing a costume and walking in public to promote something.
Task automatability	claude-haiku-4-5-20251001	1/5	The task requires physical presence in public spaces wearing costumes or signs to promote goods/services. Current AI systems cannot operate physical bodies or costumes in real-world environments, and no meaningful portion of the core task can be automated.
Task automatability	claude-sonnet-5	1/5	This requires a physical human body to wear a costume/sign and walk in public spaces; no AI system can perform this physical presence task at all.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: human physical presence is inherent to the task definition, customer engagement typically requires human interaction and presence, and organizational expectations center on visible human promoters connecting with the public.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers exist, but the fundamentally physical, embodied nature of the task acts as a de facto barrier to any digital automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI cannot perform this task at all, making any cost comparison moot. The cost of any deployment (robots, avatars) would far exceed the loaded wage of human demonstrators.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI equivalent to compare cost against; a robotic solution would be far more expensive than a low-wage human promoter.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product can autonomously don costumes, carry sign boards, or walk in public to perform this task. This remains entirely dependent on human physical execution.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical costumed street promotion; this is purely a physical/embodied task outside current AI product capabilities.

Work as part of a team of demonstrators to accommodate large crowds.

CI 5–10 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	The events and promotions sector remains highly labor-dependent and resistant to automation for customer-facing roles; adoption of AI or robotic replacements for crowd-accommodation demonstrators is negligible in production settings today.
Sector adoption velocity	claude-sonnet-5	1/5	Event marketing and in-person promotions is a low-digitization, physically grounded sector with minimal AI agent deployment for this kind of work.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could minimally assist by providing real-time crowd-flow data or scheduling optimization, but the core work of being present and accommodating attendees remains human-driven. Limited augmentation potential given the task's fundamentally physical and social nature.
Augmentation potential	claude-sonnet-5	2/5	AI could help with scheduling, crowd analytics, or team coordination logistics, but offers little direct assistance to the in-person demonstration activity itself.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires real-time physical presence, crowd management, interpersonal engagement, and adaptive responsiveness to diverse attendees—capabilities far beyond current AI systems. No meaningful automation is achievable for the core demand of accommodating crowds as part of a team.
Task automatability	claude-sonnet-5	1/5	This is a physical, in-person team coordination task requiring human presence to engage crowds; no AI system can perform this end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: event venues typically require licensed staff for safety compliance, insurance and liability concerns around crowd management, and fundamental customer expectation that demonstrators are human. Organizational preference for human presence is considerable.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but strong customer preference for human interaction and physical presence at events creates real friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The on-site human labor cost for crowd accommodation and team coordination is minimal compared to the infrastructure needed to deploy equivalent robotic or AI systems capable of managing large crowds in real time.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical crowd-facing role, so AI cost comparison is not applicable; the human remains the only option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product performs crowd accommodation or team-based demonstrator work. This requires embodied presence, real-world spatial reasoning, and human social interaction that are not yet reliably automated in production systems.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for physical demonstrators working live crowds; this remains entirely human-performed in practice.

Related occupations — Sales & Related

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.