Building cleaning workers, all other
SOC Code: 37-2019
Building cleaning workers, all other carries a 35% AI exposure score (Medium automation risk), with a median annual wage of $42,360 and +2.5% projected employment growth from 2024 to 2034 (BLS), affecting approximately 18,100 workers. Full task breakdown, skills, and employer data are below.
Proportion of tasks susceptible to AI automation (O*NET analysis)
AI Exposure vs Industry Growth
Total occupations tracked
832
Covering all SOC major groups
Data currency
2024
BLS Employment Projections
AI exposure avg
40%
Fleet-wide median across all roles
Composite score weighing O*NET task data completeness, BLS projection methodology, and cross-validation with employer risk grades.
Employment Projections
Occupation Insight
Building cleaning workers, all other (SOC 37-2019) carries an AI exposure score of 35%, placing it in the Medium automation-risk tier. This score is computed from O*NET Database 30.0 task-level analysis, where each task an 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 economic context matters alongside the risk score. BLS counted approximately 18,100 workers in this occupation in 2024, and projects a +2.5% change through 2034 — modest growth that keeps the occupation viable even as tasks evolve. Median annual compensation stands at $42,360, reflecting both skill scarcity and the value employers place on the tasks that remain difficult to automate. Entry typically requires No formal educational credential, plus None of related experience.
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 Building cleaning workers, all other. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure. Pair the AI exposure score with the BLS employment projection and wage percentiles above for a complete career assessment.
Education & Entry Requirements
Frequently Asked Questions
Will AI replace Building cleaning workers, all other?
Building cleaning workers, all other has an AI exposure score of 35%, 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 Building cleaning workers, all other?
According to BLS Employment Projections 2024-2034, Building cleaning workers, all other is projected to grow by 2.5% over the decade. Current employment stands at approximately 18,100 workers.
What skills are needed for Building cleaning workers, all other?
Building cleaning workers, all other requires a combination of technical knowledge and interpersonal skills. Typical education requirement: No formal educational credential.
How much do Building cleaning workers, all other earn?
The median annual wage for Building cleaning workers, all other is $42,360, 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 Building cleaning workers, all other?
The typical entry-level education for Building cleaning workers, all other is No formal educational credential. Employers generally expect None of related work experience. On-the-job training typically involves Short-term on-the-job training. Requirements can vary by employer and specialization.
Which companies employ Building cleaning workers, all other?
Building cleaning workers, all other 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 O*NET task analysis. Most tasks require human judgment and are resistant to automation.
Related Occupations
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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).