Somewhat Resilient

Last Update: 8/30/2026

AI Resilience Score for Machine Servicers/Repairers:

44.6%

Median Score

Meaningful human contribution

Med

Long-term employer demand

Low

Sustained economic opportunity

Low

Our confidence in this score:
Medium-high

Contributing sources

Methodology and Scoring Rationale

To score how resilient coin, vending, and amusement machine repair work is to AI, we ask one question in three parts:

First, how much of the job still needs a human, read from five AI-exposure sources: our own AI Resilience Model, Anthropic's Observed Exposure, Microsoft's AI Applicability, Will Robots Take My Job, and OpenAI Signals. We call this dimension Meaningful Human Contribution (MHC) and weight it at 40%.

Next, whether employers will keep hiring for this job over the long term. This dimension, which we call Long-term Employer Demand (LTE), is calculated from BLS data and weighted at 30%.

Last, whether pay and mobility will hold up. We use wage bill and adaptive capacity data from independent researchers (Althoff & Reichardt, 2026; Manning & Aguirre, 2026). We call this dimension Sustained Economic Opportunity (SEO) and weight it at 30%.

For machine servicers and repairers, 6 of 8 sources had data. On AI exposure, AI Resilience Model, Microsoft, and OpenAI Signals all agreed the hands-on repair work stays human, while Will Robots Take My Job saw it differently, landing confidence at medium-high. Weaker hiring and pay signals pulled the score down, leaving this role "Somewhat Resilient."

AI Resilience Report forCoin, Vending, and Amusement Machine Servicers and Repairers

$47,450 median salary3,800 annual openingsSOC Code: 49-9091.00

Coin, Vending, and Amusement Machine Servicers and Repairers are somewhat less resilient to AI impacts than most occupations, according to our analysis of 6 sources.

This career is labeled "Somewhat Resilient" because AI is genuinely changing how the job works, even if it is not eliminating it. The back-office side of things (tracking parts, logging maintenance records, and spotting equipment problems remotely) is being handled more and more by AI-powered monitoring systems, which means some of the easier, routine tasks are shifting away from human hands.

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This role is somewhat resilient

This career is labeled "Somewhat Resilient" because AI is genuinely changing how the job works, even if it is not eliminating it. The back-office side of things (tracking parts, logging maintenance records, and spotting equipment problems remotely) is being handled more and more by AI-powered monitoring systems, which means some of the easier, routine tasks are shifting away from human hands.

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Analysis of Current AI Resilience

Machine Servicers/Repairers

Updated Quarterly

Analysis
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State of Automation

How is AI changing Machine Servicers/Repairers jobs?

Right now, AI is mostly augmenting vending and amusement machine repair work rather than replacing it. The back-office tasks—ordering parts, keeping maintenance records, and logging transactions—are the easiest to automate because connected machines already report data automatically. According to Vending Times, AI-enabled diagnostics are allowing faster mitigation of equipment performance issues, and Aramark is using artificial intelligence to identify potential equipment issues earlier, enabling faster response times and significantly reducing machine downtime.

In practical terms, if a card reader fails, the service team can know before the next complaint, and if a chilled machine shows a temperature issue, that can be flagged quickly. Researchers are also validating this shift: an arXiv study on smart-vending predictive maintenance [1] showed IoT sensors and machine-learning models can forecast failures before they happen, cutting downtime and unnecessary service trips. But the hands-on tasks—swapping magnetic heads, soldering boards, oiling motors—still need a human.

Technicians of America [2] notes that technician roles are among the most AI-resistant jobs because of their hands-on, site-specific nature, and that AI mainly repositions technicians as problem-solvers who verify diagnoses and execute repairs.

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AI Adoption

How fast is AI adoption growing for Machine Servicers/Repairers?

Adoption of AI-powered monitoring is moving quickly on the operator side because the tools are already commercially available and cheap to bolt onto connected machines. The 2026 NAMA show report from Kiosk Industry [3] highlighted a hybrid edge-and-cloud AI architecture demonstrated by multiple exhibitors for remote monitoring, dashboards, and analytics. Yet AI is not eliminating repair jobs.

Vending Times reports [4] that today's technicians are expected to troubleshoot cashless payment systems, wireless communications, telemetry platforms, and touchscreens, and a 2026 field-service survey found 63% of service leaders reported difficulty hiring technicians. That labor shortage—paired with the fact that the broader installation, maintenance, and repair category has about 608,100 openings projected each year [5] per BLS—makes operators more eager to keep skilled humans, not fewer. The bottom line: if you're curious about this career, learning both mechanical skills and digital literacy (networking, software updates, reading AI dashboards) will make you very hard to replace.

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Will AI replace Machine Servicers/Repairers?

Will AI replace Machine Servicers/Repairers?

Not entirely. We think AI will take over some tasks, but not the whole job.

Our 44.6% AI Resilience Score reflects a real tension in this career: the back-office work is automating fast, but the hands-on repair work is holding firm. AI-enabled diagnostics can already flag a failing card reader or a temperature issue in a chilled machine before a customer complains [4], and predictive maintenance models using IoT sensors are cutting unnecessary service trips [1]. That part of the job is genuinely shrinking.

What stays human is the physical work: swapping components, soldering boards, oiling motors, and troubleshooting on-site problems that no algorithm can fix remotely. Technician roles rank among the most AI-resistant precisely because they are hands-on and site-specific [2]. The 2026 NAMA show also showed AI tools repositioning technicians as problem-solvers who verify diagnoses and execute repairs, not replacing them [3].

The economic picture is the harder part. Long-term employer demand and earning potential score low on our scorecard, so this is not a career to coast in. The workers who will do best are those who pair mechanical skills with digital literacy, reading AI dashboards, managing telemetry platforms, and updating software, making themselves genuinely hard to replace.

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Latest AI news for Machine Servicers/Repairers

The recommended articles emphasize that careers in coin, vending, and amusement machine servicing are resilient to AI disruption. For example, the article from aijobriskcheck.com highlights that the role relies heavily on physical presence and mechanical skills, making it less susceptible to automation. Additionally, the assessment.com piece outlines essential skills, salary expectations, and job outlook, providing valuable insights for aspiring professionals. This information encourages students to pursue this career with confidence, knowing that hands-on repair work will remain essential despite technological advancements.

More Career Info

Career: Coin, Vending, and Amusement Machine Servicers and Repairers

They fix and maintain vending machines and arcade games, ensuring they work properly and people can enjoy using them.

Employment & Wage Data

Median Wage

$47,450

Jobs (2025)

34,700

Growth (2025-35)

-3.4%

Annual Openings

3,800

Education

High school diploma or equivalent

Experience

None

Source: Bureau of Labor Statistics, Employment Projections 2025-2035

Task-Level AI Resilience Scores

AI-generated estimates of task resilience over the next 3 years

1

95% ResilienceSupplemental

Install machines, making the necessary water and electrical connections in compliance with codes.

2

95% ResilienceSupplemental

Transport machines to installation sites.

3

94% ResilienceSupplemental

Disassemble and assemble machines, according to specifications and using hand and power tools.

4

93% ResilienceSupplemental

Make service calls to maintain and repair machines.

5

92% ResilienceCore Task

Clean and oil machine parts.

6

92% ResilienceCore Task

Adjust and repair coin, vending, or amusement machines and meters and replace defective mechanical and electrical parts, using hand tools, soldering irons, and diagrams.

7

90% ResilienceCore Task

Replace malfunctioning parts, such as worn magnetic heads on automatic teller machine (ATM) card readers.

Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.

The AI Resilience Report is a project from CareerVillage.org®, a registered 501(c)(3) nonprofit.

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