Last Update: 2/17/2026
Your role’s AI Resilience Score is
Median Score
Changing Fast
Evolving
Stable
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
What does this resilience result mean?
These roles are expected to remain steady over time, with AI supporting rather than replacing the core work.
AI Resilience Report for
They keep buildings clean by sweeping, mopping, and dusting, making sure everything looks tidy and welcoming.
This role is stable
Building cleaning jobs are considered stable because, while robots can help with big or repetitive tasks like vacuuming large floors or cleaning high windows, they can't handle everything. Many cleaning chores still need human skills like judgment, flexibility, and attention to detail.
Read full analysisLearn more about how you can thrive in this position
Learn more about how you can thrive in this position
This role is stable
Building cleaning jobs are considered stable because, while robots can help with big or repetitive tasks like vacuuming large floors or cleaning high windows, they can't handle everything. Many cleaning chores still need human skills like judgment, flexibility, and attention to detail.
Read full analysisContributing Sources
We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.
AI Resilience
AI Resilience Model v1.0
AI Task Resilience
Medium Demand
We use BLS employment projections to complement the AI-focused assessments from other sources.
Learn about this scoreGrowth Rate (2024-34):
Growth Percentile:
Annual Openings:
Annual Openings Pct:
Analysis of Current AI Resilience
Building Cleaning Workers
Updated Quarterly • Last Update: 2/17/2026

What's changing and what's not
Cleaners already use some machines for big jobs, but robots do only part of the work. For example, specialized window‐cleaning robots are becoming real – they can climb skyscrapers and wash glass without putting people in danger [1]. There are also robot vacuums and autonomous floor‐scrubbers used in malls or airports for routine cleaning.
However, most detailed tasks – like scrubbing tough spots, moving furniture, or organizing supplies – still rely on humans. In fact, O*NET notes this “all other” cleaning category covers a wide range of duties and has no single standard skill set [2]. U.S. data show about 17,000 jobs in this category (as of 2020) with a mean wage of about \$18.68/hour [3], reflecting that cleaning work is labor‐intensive.
In short, while machines can handle repetitive floors and high windows, many everyday cleaning chores remain manual for now.

AI in the real world
Whether cleaning firms add AI robots depends on costs and needs. On one hand, robots can save money over time: the median cleaner earns roughly \$34,500 per year (about \$16.59/hour) [3], so a commercial cleaning robot (costing tens of thousands up front) could “pay for itself” if it runs many hours. On the other hand, the big upfront price and maintenance can slow adoption in smaller businesses.
News reports note that companies usually turn to automation when they can’t find enough people【1†】. Labor shortages (especially during the pandemic) have pushed some hospitals and hotels to try robot assistants for tasks like disinfection or vacuuming, but they often still need human supervisors. Socially, many clients prefer a person who can see and fix every little problem – trust and quality matter.
In practice, experts expect AI to augment rather than replace cleaning workers. Robots can boost safety and efficiency (for example, window‐washing bots reduce fall risk [1]), but human skills like judgment, flexibility, and care remain crucial. In short, cleaning jobs may change (with more tech help), but people will still do most of the work for the foreseeable future [1]【1†】.

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Median Wage
$42,360
Jobs (2024)
18,100
Growth (2024-34)
+2.5%
Annual Openings
2,600
Education
No formal educational credential
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034

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