Shoe and leather workers and repairers
SOC Code: 51-6041
Shoe and leather workers and repairers carries a 28% AI exposure score (Medium automation risk), with a median annual wage of $35,950 and -3.8% projected employment growth from 2024 to 2034 (BLS), affecting approximately 9,500 workers. Full task breakdown, skills, and employer data are below.
Proportion of tasks susceptible to AI automation (O*NET analysis)
At 28% AI exposure, Shoe and leather workers and repairers sits 9 points below the 37.2% average across 832 U.S. occupations - more exposed than 23% of them. Most of its core tasks still require human judgment.
Career outlook score
31/100
Shoe and leather workers and repairers, weighted across 3 of 3 tracked dimensions
A weighted composite of median wage, projected growth, and AI-automation security (inverted exposure), each benchmarked against every other tracked occupation. It describes this occupation's standing across those three dimensions, not a guarantee of future outcomes.
- Median wage F $35,950
Annual median wage, percentile rank vs all tracked occupations
- Projected growth F -3.8%
Projected employment change, 2024-2034, BLS Employment Projections
- AI exposure (inverted) B+ 28% exposure
Lower O*NET task-automability scores lower risk; this dimension scores security, not exposure
Home health and personal care aides is #1 of 832 occupations by employment (4.3M, AI exposure rank #392 at 35%) vs. Bookkeeping, accounting, and auditing clerks #1 by AI task exposure (95%, employment rank #17 at 1.6M).
Automation-risk inventory
Exclusive cut of 832 occupations by PlainWorkforce automation-risk band. Orthogonal to the continuous AI-exposure distribution chart below and to this occupation's single risk badge, this is the corpus band mix, with Shoe and leather workers and repairers in the Medium band.
Credential × growth standing
Open entry with non-declining BLS pace
Shoe and leather workers and repairers keeps an HS / some college bar (333 of 832) and sits in Slow growth (245 of 832). Lower formal gates with a non-shrinking employment line - skill and AI desks matter more than credentials here.
Where Shoe and leather workers and repairers sits among all occupations
AI-exposure score distribution across 832 U.S. occupations (O*NET task analysis). This occupation is marked in rose.
Where Shoe and leather workers and repairers sits by AI exposure
AI-exposure score distribution across U.S. occupations (O*NET task analysis)
28% Among the lowest lower than 77% of 832 occupations
Occupations, banded by AI-exposure score
Each bar is a band; taller bars hold more occupations. The dashed line + filled bar mark this entry. Hover or tap any bar for its full count and share, and where it sits relative to this entry.
Source O*NET task data (U.S. Department of Labor) · BLS 2024–2034
Source: O*NET task data (U.S. Department of Labor); AI-exposure scores are PlainWorkforce's analysis. As of BLS 2024–2034.
Shoe and leather workers and repairers vs. its occupational neighbors
AI exposure (horizontal) vs projected 2024–2034 growth (vertical) for occupations in the same SOC group. Shoe and leather workers and repairers is marked in rose; the top-left corner is the most future-proof.
Source: BLS Employment Projections 2024–2034 (growth) and O*NET (AI-exposure analysis by PlainWorkforce).
Employment Projections
Where This Score Comes From
Shoe and leather workers and repairers's Medium automation-risk tier is computed from O*NET Database 30.0 task-level analysis, where each documented task the occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score below 40% reflects tasks anchored in physical dexterity, unstructured environments, or high-touch human interaction that current AI cannot reliably replicate.
The employment, wage, and education figures above come from a second, separate federal source: BLS Employment Projections 2024–2034, matched to the O*NET task data by Standard Occupational Classification (SOC 51-6041) code, with the wage figure cross-validated against the BLS Occupational Employment and Wage Statistics (OEWS) May 2024 survey. BLS figures are presented exactly as published, with no adjustment on our part; the SOC-code match is what lets Shoe and leather workers and repairers be compared directly against every other tracked occupation on the same basis, not just roles that happen to share a job title.
For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Shoe and leather workers and repairers. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure.
Education & Entry Requirements
Top Tasks (O*NET)
- 1. Prepare inserts, heel pads, and lifts from casts of customers' feet.
- 2. Dress and otherwise finish boots or shoes, as by trimming the edges of new soles and heels to the shoe shape.
- 3. Attach insoles to shoe lasts, affix shoe uppers, and apply heels and outsoles.
- 4. Clean and polish shoes.
- 5. Cement, nail, or sew soles and heels to shoes.
- 6. Check the texture, color, and strength of leather to ensure that it is adequate for a particular purpose.
- 7. Dye, soak, polish, paint, stamp, stitch, stain, buff, or engrave leather or other materials to obtain desired effects, decorations, or shapes.
- 8. Shape shoe heels with a knife, and sand them on a buffing wheel for smoothness.
- 9. Place shoes on lasts to remove soles and heels, using knives or pliers.
- 10. Repair or replace soles, heels, and other parts of footwear, using sewing, buffing and other shoe repair machines, materials, and equipment.
Key Skills Required
- Active Listening
- Speaking
- Critical Thinking
- Monitoring
- Reading Comprehension
- Social Perceptiveness
- Coordination
- Service Orientation
- Operations Monitoring
- Judgment and Decision Making
Knowledge Areas
- Production and Processing
- Customer and Personal Service
- Mechanical
- Sales and Marketing
- Administration and Management
- Education and Training
- Engineering and Technology
- Economics and Accounting
- Design
- English Language
Frequently Asked Questions
Will AI replace Shoe and leather workers and repairers?
Shoe and leather workers and repairers has an AI exposure score of 28%, indicating a medium level of automation risk. The majority of tasks in this role require human judgment, creativity, or physical presence that AI cannot easily replicate.
What is the job outlook for Shoe and leather workers and repairers?
According to BLS Employment Projections 2024-2034, Shoe and leather workers and repairers is projected to decline by 3.8% over the decade. Current employment stands at approximately 9,500 workers.
What skills are needed for Shoe and leather workers and repairers?
Key skills for Shoe and leather workers and repairers include Active Listening, Speaking, Critical Thinking, and others. Typical entry-level education is High school diploma or equivalent.
How much do Shoe and leather workers and repairers earn?
The median annual wage for Shoe and leather workers and repairers is $35,950, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.
What education is required for Shoe and leather workers and repairers?
The typical entry-level education for Shoe and leather workers and repairers is High school diploma or equivalent. Employers generally expect None of related work experience. On-the-job training typically involves Moderate-term on-the-job training. Requirements can vary by employer and specialization.
Which companies employ Shoe and leather workers and repairers?
Shoe and leather workers and repairers roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.
AI Exposure Rating
Medium automation risk based on 10 analyzed tasks. Most tasks require human judgment and are resistant to automation.
Nationwide occupations with similar workforce profiles
Two data-derived peer sets for Shoe and leather workers and repairers: AI-exposure neighbors and BLS 2034 growth neighbors (distinct comparison neighborhoods, cross-occupation).
Similar AI exposure score
Nearest O*NET task-automability scores nationwide (28% here).
- Acupuncturists · AI exposure 28%
- Advertising and promotions managers · AI exposure 28%
- Agricultural inspectors · AI exposure 28%
- Carpenters · AI exposure 28%
Similar projected growth (2024–2034)
Nearest BLS employment change rates nationwide (-3.8% here).
- Molding, coremaking, and casting machine setters, operators, and tenders, metal and plastic · growth -3.8%
- Computer user support specialists · growth -3.7%
- Insurance claims and policy processing clerks · growth -3.7%
- News analysts, reporters, and journalists · growth -3.9%
Peers are nearest-neighbor matches on published BLS + O*NET metrics among occupations with ≥10,000 workers in 2024; corpus ranks above sort the full tracked set.
Related Occupations
Showing 6 of 81 occupations with a comparable automation-risk profile.
What to do with this
Use Shoe and leather workers and repairers' numbers above to compare, not just read in isolation.
- Compare Shoe and leather workers and repairers side by side against another occupation on wage, growth, and AI exposure. Workforce comparison tool
- Understand how the AI exposure score above is actually calculated before treating it as a verdict. Reading AI exposure scores
- See the full ranked list of occupations with the lowest automation exposure nationwide. Safest occupations from AI
AI exposure scores estimate task automatability from O*NET data; they are not a certainty of job loss, and BLS growth projections are estimates, not guarantees.
Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).