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# Will cleaning equipment operators be replaced by AI?

Updated October 2026

Cleaning equipment operators score 25 out of 100 for AI exposure. 57% of their task time needs a person. AI can do 10% and helps with 33%.

-   **10%** AI can do it
-   **33%** AI helps
-   **57%** Needs a person

Low exposure

Lower than 71% of the 923 jobs we scored.

## Task by task

We graded 11 tasks, most exposed first. AI can do 1 of them, helps with 3 and leaves 7 to people.

| Task | AI exposure |
| --- | --- |
| Record gauge readings, materials used, processing times, or test results in production logs. Logging readings and test results is routine data recording. | 80% AI can do it |
| Set controls to regulate temperature and length of cycles, and start conveyors, pumps, agitators, and machines. Digital controllers can set cycles, but a person verifies settings. | 50% AI helps |
| Observe machine operations, gauges, or thermometers, and adjust controls to maintain specified conditions. Sensors and alerts help monitor, but adjustments need a person. | 40% AI helps |
| Add specified amounts of chemicals to equipment at required times to maintain solution levels and concentrations. Dosing can be automated, but physical chemical handling stays manual. | 30% AI helps |
| Operate or tend machines to wash and remove impurities from items such as barrels or kegs, glass products, tin plate surfaces, dried fruit, pulp, animal stock, coal, manufactured articles, plastic, or rubber. Machine washing needs physical handling and presence on the floor. | 15% Needs a person |

## How cleaning equipment operators compare

All jobs (923)

Production (107)

The dark line is **cleaning equipment operators, at 25**. Each dot is a score, and darker dots mean more jobs share it. The short marks are the averages, 37 for all jobs and 22 for production.

## Pay and outlook in the US

Median pay

$43,530 a year, May 2025

People employed

14,760 in the US, May 2025

Projected change in jobs

+3.4% from 2024 to 2034

## Skills to build

-   Keep production logs of gauge readings in Microsoft Excel
-   Write shift handover and deviation reports in Microsoft Word
-   Verify digital controller cycle settings against the process sheet
-   Inspect washer pumps, spray nozzles, and conveyors for malfunctions on the floor
-   Mix and dose cleaning solutions with calibrated measuring tanks
-   Grease and adjust machine mechanical parts with hand tools

## Safer roles nearby

5 related jobs with lower scores.

-   [Separating machine operators20 out of 100](https://www.aigentcy.com/ai-exposure/separating-machine-operators/)
-   [Mixing machine operators24 out of 100](https://www.aigentcy.com/ai-exposure/mixing-machine-operators/)
-   [Laundry workers13 out of 100](https://www.aigentcy.com/ai-exposure/laundry-workers/)
-   [Vehicle cleaners13 out of 100](https://www.aigentcy.com/ai-exposure/vehicle-cleaners/)
-   [Refrigeration operators21 out of 100](https://www.aigentcy.com/ai-exposure/refrigeration-operators/)

## Questions about cleaning equipment operators and AI

Will AI replace cleaning equipment operators?

Partly. About 3 in 10 working hours go to tasks AI can already do, mostly recording readings in production logs and setting machine controls. Washing, loading, and floor inspection stay with people because they need hands and presence at the machine.

Which cleaning equipment operator tasks is AI taking over?

Logging gauge readings, materials used, and test results in production logs scores 80 on exposure, the highest in this job. Setting temperature and cycle controls follows at 50, since digital controllers can run the cycle while a person verifies the settings.

Which cleaning equipment operator tasks stay human?

Loading and unloading objects onto racks scores 5 on exposure, and cleaning or lubricating machine parts with hand tools scores 3. Both need hands on the equipment, and software has no way to do that physical work.

How can cleaning equipment operators stay valuable?

Keep exact readings in Microsoft Excel and flag any drift before it becomes a failed batch. Check digital controller settings before each cycle, inspect pumps and nozzles on the floor, dose chemicals by calibrated tanks, and write shift reports in Microsoft Word.

## Related jobs

-   [Kiln and furnace operators26 out of 100](https://www.aigentcy.com/ai-exposure/kiln-and-furnace-operators/)
-   [Dye machine operators29 out of 100](https://www.aigentcy.com/ai-exposure/dye-machine-operators/)
-   [Chemical plant operators24 out of 100](https://www.aigentcy.com/ai-exposure/chemical-plant-operators/)
-   [Crushing and grinding machine operators26 out of 100](https://www.aigentcy.com/ai-exposure/crushing-and-grinding-machine-operators/)

## Next steps for cleaning equipment operators

-   ### Check your job
    
    Your job title is a rough guide. Your tasks decide your score. Paste your job description or CV to see which tasks AI can do.
    
    [Check your job](https://www.aigentcy.com/tools/ai-exposure-checker/) [Check your CV](https://www.aigentcy.com/tools/cv-checker/)
    
-   ### Estimate team savings
    
    The score is per person. Add headcount and salary to see the hours AI can take on in a team of cleaning equipment operators, and what they cost.
    
    [Estimate what AI saves a team of cleaning equipment operators](https://www.aigentcy.com/tools/team-ai-audit/?role=cleaning-equipment-operators) [Work out the return on automation](https://www.aigentcy.com/tools/ai-savings-calculator/)
    

### Check which roles in your team AI can take on

We map your team's tasks, show what AI can do today and build the automations that pay back first.

[Book an AI audit](https://www.aigentcy.com/audit/)

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-   [**Jobs A to Z**Every job we score, by first letter.](https://www.aigentcy.com/ai-exposure/jobs/)
-   [**AI exposure by industry**Average scores for 22 job groups.](https://www.aigentcy.com/ai-exposure/industries/)
-   [**Write a CV screening software can read**How applicant tracking systems read a CV, and what to change.](https://www.aigentcy.com/ai-exposure/cv-screening-guide/)
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-   [**How we score AI exposure**The grading, the weights and every source.](https://www.aigentcy.com/ai-exposure/methodology/)

Sources and licences

Occupation data from the O\*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and modified by Aigentcy. O\*NET® is a trademark of USDOL/ETA. Wage, employment and projection data from the U.S. Bureau of Labor Statistics. EU occupation names from ESCO, European Commission. AI usage signals from the Anthropic Economic Index (CC BY) and Microsoft Research (CC BY 4.0). [Methodology and sources](https://www.aigentcy.com/ai-exposure/methodology/). Scores © Aigentcy. Quote them with a link to the page.
