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Floor Sanders and Finishers

Occupation · SOC 47-2043.00

Scrape and sand wooden floors to smooth surfaces using floor scraper and floor sanding machine, and apply coats of finish.

Also called: Floor Finisher · Floor Mechanic · Floor Sander · Hardwood Floor Sander · Finisher · Floor Refinisher · Floor Sander and Finisher · Hardwood Floor Finisher and Sander · Hardwood Floor Refinisher · Sander · Bowling Alley Refinisher · Floor Renovator

Job family: Construction and Extraction Occupations

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A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-47-2043-00/context.md directly.

AI work map

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.

4th-percentile task overlap — yet about 400 openings a year (+2.6% projected, BLS) . What exposure means →

AI & job outlook

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.

Exposure to current AI

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.) Low 3rd -1.7
LLM task exposure, γ (OpenAI / Eloundou) Low 19th 0.1
AI assistant applicability (Microsoft) Low 0th 0.0

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). 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.

Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.

Historical automation estimate (2013)

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 0.9 · 74th percentile among occupations · High

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook About average · +2.6% by 2034
Projected annual openings 400
Employment 2024 → 2034 5,600 → 5,800

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

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.

10% mean task exposure (2025)
3rd percentile of 427 placed occupations
+0 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Floor Layers and Tile Setters · 7122 10% Not exposed

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.

Tasks

All 7 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Building and Construction 4.1
Customer and Personal Service 4.1
English Language 3.6
Production and Processing 3.3
Administration and Management 3.0
Mechanical 3.0
Mathematics 2.9
Design 2.8

Abilities

Arm-Hand Steadiness 3.8
Manual Dexterity 3.6
Control Precision 3.6
Multilimb Coordination 3.6
Trunk Strength 3.6
Finger Dexterity 3.3
Stamina 3.3
Near Vision 3.3
Oral Comprehension 3.1
Static Strength 3.1
Extent Flexibility 3.1
Oral Expression 3.0
Selective Attention 3.0
Reaction Time 3.0
Dynamic Strength 3.0
Information Ordering 2.9
Visualization 2.9
Gross Body Coordination 2.9
Speech Recognition 2.9
Problem Sensitivity 2.8
Deductive Reasoning 2.8
Inductive Reasoning 2.8
Category Flexibility 2.8

Transferable skills

Operation and Control 3.1
Coordination 3.0
Operations Monitoring 2.9
Time Management 2.9
Complex Problem Solving 2.8

Essential skills

Active Listening 3.0
Monitoring 2.9
Speaking 2.8
Critical Thinking 2.8

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Floor planning software Computer aided design CAD software
FloorCOST Estimator for Excel Project management software
Flooring Technologies QFloors Data base user interface and query software
Measure Square Project management software
Pacific Solutions FloorRight Project management software
Vimeo Video creation and editing software

Work context

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.

Exposed to Contaminants 4.9
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.9
Face-to-Face Discussions with Individuals and Within Teams 4.7
Spend Time Bending or Twisting Your Body 4.7
Spend Time Making Repetitive Motions 4.6
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.6
Spend Time Kneeling, Crouching, Stooping, or Crawling 4.6
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.5
Freedom to Make Decisions 4.5
Impact of Decisions on Co-workers or Company Results 4.4
Importance of Being Exact or Accurate 4.4
Determine Tasks, Priorities and Goals 4.3
Indoors, Environmentally Controlled 4.2
Spend Time Walking or Running 4.2
Spend Time Standing 4.1
Frequency of Decision Making 4.0
Exposed to Hazardous Equipment 4.0
Work Outcomes and Results of Other Workers 4.0
Time Pressure 4.0
Telephone Conversations 4.0
Contact With Others 4.0
Exposed to Whole Body Vibration 3.8
Work With or Contribute to a Work Group or Team 3.7
Health and Safety of Other Workers 3.6
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 3.5
Coordinate or Lead Others in Accomplishing Work Activities 3.5
Importance of Repeating Same Tasks 3.4
Exposed to Cramped Work Space, Awkward Positions 3.2
Level of Competition 3.2
Exposed to Hazardous Conditions 3.1
Deal With External Customers or the Public in General 3.1
Pace Determined by Speed of Equipment 3.1
Physical Proximity 3.0
Consequence of Error 2.9
Exposed to Minor Burns, Cuts, Bites, or Stings 2.9
Indoors, Not Environmentally Controlled 2.8
In an Enclosed Vehicle or Operate Enclosed Equipment 2.8
Spend Time Keeping or Regaining Balance 2.6
Exposed to Very Hot or Cold Temperatures 2.5
Dealing With Unpleasant, Angry, or Discourteous People 2.4

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
No formal educational credential · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Construction Trades . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

Less than a High School Diploma 46.6%
High School Diploma 34.6%
Post-Secondary Certificate 16.4%
Some College Courses 2.3%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 3.3
Enterprising 1.5
Social 1.4
Investigative 1.4
Artistic 1.4

Interest areas

Construction/Woodwork 6.4
Physical/Manual Labor 6.3
Mechanics/Electronics 1.8
Transportation/Machine Operation 1.5
Engineering 1.3
Applied Arts and Design 1.2
Management/Administration 1.2

Work styles

Attention to Detail 2.1
Dependability 2.1
Cautiousness 1.4

Wages & employment

U.S. · annual wages (BLS OEWS)

$36k10th$41k25th$49kMedian$58k75th$67k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
6k20246k2034 (proj.)+2.6% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $35,790
25th percentile $41,320
Median (50th) $49,150
75th percentile $58,230
90th percentile $66,510
People employed 4,140

Industries that employ this occupation

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
Construction · Sector 3,840 $49,490
Poured Concrete Foundation and Structure Contractors · National industry 210 $50,310
Manufacturing · Sector 100 $47,410
Administrative and Support and Waste Management and Remediation Services · Sector 100 $37,910
Retail Trade · Sector 80 $54,340
Temporary Help Services · National industry — $37,910

Where this work is most concentrated

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
Poured Concrete Foundation and Structure Contractors · National industry 30.23× 210
Construction · Sector 17.61× 3,840
Administrative and Support and Waste Management and Remediation Services · Sector 0.41× 100
Manufacturing · Sector 0.29× 100

Part of the Construction career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Floor Sanders and Finishers sits at the 4th percentile of AI task-overlap and the 32nd percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Floor Sanders and Finishers Cement Masons and Concrete Finishers Drywall and Ceiling Tile Installers Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters Brickmasons and Blockmasons Furniture Finishers AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Floor Sanders and Finishers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 3rd percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Floor Sanders and Finishers show 4th-percentile AI task overlap — and about 400 annual U.S. openings

  • Floor Sanders and Finishers rank in the 4th percentile (Low 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 400 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 about average (+2.6%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $49,150, across about 4,140 U.S. workers.BLS OEWS (May 2024)
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Floor Sanders and Finishers show 4th-percentile AI task overlap — and about 400 annual U.S. openings

• Floor Sanders and Finishers rank in the 4th percentile (Low 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 400 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 about average (+2.6%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $49,150, across about 4,140 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Floor Sanders and Finishers". https://singulariki.com/roles/role-47-2043-00
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.

Sources for this page

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.

Cite this page
Plain

Singulariki. "Floor Sanders and Finishers." 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; 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-47-2043-00

APA

Singulariki. (2026). Floor Sanders and Finishers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-47-2043-00

BibTeX 
@misc{singulariki-role-47-2043-00,
  title  = {Floor Sanders and Finishers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; 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-47-2043-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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