Occupation · SOC 41-9011.00
Demonstrate merchandise and answer questions for the purpose of creating public interest in buying the product. May sell demonstrated merchandise.
Also called: Demonstrator · In Store Demonstrator · Merchandiser · Product Demonstrator · Brand Ambassador · Demo Specialist (Demonstration Specialist) · Event Specialist · Field Merchandiser · Food Demonstrator · Product Ambassador · Appliance Counselor · Bell Ringer
Job family: Sales and Related Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-41-9011-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
54th-percentile task overlap — yet about 14,000 openings a year (-0.1% projected, BLS), and observed AI use leans 4817% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 51st | 0.1 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 25th | 0.2 | |
| AI assistant applicability (Microsoft) High | 90th | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.5 · 47th percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Learn about competitors' products or consumers' interests or concerns to answer questions or provide more complete information. | 2.1% | |
| Prepare or alter presentation contents to target specific audiences. | 2.1% | |
| Suggest specific product purchases to meet customers' needs. | 1.6% | |
| Demonstrate or explain products, methods, or services to persuade customers to purchase products or use services. | 0.3% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -0.1% by 2034 |
| Projected annual openings | 14,000 |
| Employment 2024 → 2034 | 79,200 → 79,100 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Sales Demonstrators · 5242 | 42% | Gradient 2 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 48.2% working with AI · 43.6% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 14.6% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Suggest specific product purchases to meet customers' needs. | Directive | 2.8% |
| Demonstrate or explain products, methods, or services to persuade customers to purchase products or use services. | Iteration | 2.2% |
| Provide product information, using lectures, films, charts, or slide shows. | Directive | 0.9% |
| Prepare or alter presentation contents to target specific audiences. | Iteration | 0.6% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Demonstrate or explain products, methods, or services to persuade customers to purchase products or use services. | 100.0% | |
| Prepare or alter presentation contents to target specific audiences. | 100.0% | |
| Suggest specific product purchases to meet customers' needs. | 99.6% | |
| Provide product information, using lectures, films, charts, or slide shows. | 98.9% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me suggest specific product purchases to meet customers' needs. From: Suggest specific product purchases to meet customers' needs. · 2.8% of measured AI use · directive
Help me demonstrate or explain products, methods, or services to persuade customers to purchase products or use services. From: Demonstrate or explain products, methods, or services to persuade customers to purchase products or use services. · 2.2% of measured AI use · task iteration
Help me provide product information, using lectures, films, charts, or slide shows. From: Provide product information, using lectures, films, charts, or slide shows. · 0.9% of measured AI use · directive
Help me prepare or alter presentation contents to target specific audiences. From: Prepare or alter presentation contents to target specific audiences. · 0.6% of measured AI use · task iteration
All 24 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Customer and Personal Service | 4.0 | |
| Sales and Marketing | 3.7 | |
| English Language | 3.7 | |
| Food Production | 3.3 | |
| Public Safety and Security | 3.0 |
| Active Listening | 3.9 | |
| Speaking | 3.9 | |
| Reading Comprehension | 3.3 | |
| Writing | 3.0 | |
| Critical Thinking | 3.0 | |
| Monitoring | 3.0 | |
| Active Learning | 2.9 | |
| Learning Strategies | 2.8 |
| Oral Comprehension | 3.9 | |
| Oral Expression | 3.9 | |
| Speech Clarity | 3.9 | |
| Speech Recognition | 3.8 | |
| Deductive Reasoning | 3.1 | |
| Near Vision | 3.1 | |
| Written Comprehension | 3.0 | |
| Written Expression | 3.0 | |
| Problem Sensitivity | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Information Ordering | 3.0 | |
| Category Flexibility | 3.0 | |
| Selective Attention | 3.0 | |
| Originality | 2.9 | |
| Visualization | 2.9 | |
| Far Vision | 2.9 | |
| Fluency of Ideas | 2.8 | |
| Perceptual Speed | 2.8 | |
| Time Sharing | 2.8 | |
| Trunk Strength | 2.8 |
| Persuasion | 3.5 | |
| Service Orientation | 3.3 | |
| Social Perceptiveness | 3.0 | |
| Coordination | 3.0 | |
| Complex Problem Solving | 3.0 | |
| Judgment and Decision Making | 3.0 | |
| Time Management | 3.0 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| Example | Category | |
|---|---|---|
| Hypertext markup language HTML | Web platform development software | Hot technology |
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Windows | Operating system software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Zoom | Video conferencing software | Hot technology |
| Eko | Desktop communications software | |
| Email software | Electronic mail software | |
| Social media sites | Web page creation and editing software | |
| Web browser software | Internet browser software |
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Business, Management, Marketing, and Related Support Services . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| High School Diploma | 64.3% | |
| Less than a High School Diploma | 35.0% | |
| Master's Degree | 0.7% |
The interests and personal qualities O*NET associates with people who do this work.
| Sales | 6.0 | |
| Marketing/Advertising | 5.3 | |
| Public Speaking | 4.9 | |
| Teaching/Education | 2.4 | |
| Personal Service | 2.1 | |
| Culinary Art | 2.0 | |
| Physical/Manual Labor | 2.0 |
| Enterprising | 5.4 | |
| Conventional | 3.6 | |
| Artistic | 3.3 | |
| Social | 3.3 | |
| Realistic | 3.2 | |
| Investigative | 1.9 |
| Social Orientation | 2.7 | |
| Optimism | 2.1 | |
| Cooperation | 1.9 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $30,910 |
| 25th percentile | $33,860 |
| Median (50th) | $37,960 |
| 75th percentile | $50,100 |
| 90th percentile | $60,320 |
| People employed | 64,770 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 23,060 | $35,340 |
| Manufacturing · Sector | 11,750 | $39,420 |
| Retail Trade · Sector | 10,700 | $37,850 |
| Wholesale Trade · Sector | 4,890 | $39,500 |
| Arts, Entertainment, and Recreation · Sector | 920 | $49,890 |
| Construction · Sector | 710 | $37,610 |
| Transportation and Warehousing · Sector | 330 | $42,000 |
| Management of Companies and Enterprises · Sector | 320 | $40,800 |
| Information · Sector | 280 | $48,390 |
| Educational Services · Sector | 250 | $36,510 |
| Casino Hotels · National industry | 120 | $44,130 |
| Sporting Goods Retailers · National industry | 80 | $43,810 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 5.1× | 23,060 |
| Manufacturing · Sector | 2.19× | 11,750 |
| Wholesale Trade · Sector | 1.93× | 4,890 |
| Retail Trade · Sector | 1.63× | 10,700 |
| Casino Hotels · National industry | 0.85× | 120 |
| Arts, Entertainment, and Recreation · Sector | 0.83× | 920 |
| Management of Companies and Enterprises · Sector | 0.27× | 320 |
| Information · Sector | 0.23× | 280 |
Part of the Marketing & Sales career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Demonstrators and Product Promoters — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 79th percentile of 427 international occupations.
Demonstrators and Product Promoters show 54th-percentile AI task overlap — and about 14,000 annual U.S. openings
Demonstrators and Product Promoters show 54th-percentile AI task overlap — and about 14,000 annual U.S. openings • Demonstrators and Product Promoters rank in the 54th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 14,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-0.1%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $37,960, across about 64,770 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 48% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Demonstrators and Product Promoters". https://singulariki.com/roles/role-41-9011-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Demonstrators and Product Promoters." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-41-9011-00
Singulariki. (2026). Demonstrators and Product Promoters. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-41-9011-00
@misc{singulariki-role-41-9011-00,
title = {Demonstrators and Product Promoters},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-41-9011-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.