Last Update: 3/13/2026
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 shifting as AI becomes part of everyday workflows. Expect new responsibilities and new opportunities.
AI Resilience Report for
They keep machines running smoothly by checking, fixing, and cleaning them to prevent breakdowns and ensure everything works safely and efficiently.
This role is evolving
The career of a maintenance worker in machinery is labeled as "Evolving" because AI tools are increasingly being integrated to help with tasks like spotting issues early and assisting with material handling. However, while AI makes these jobs faster and safer, it doesn't replace the need for skilled human workers who use their senses and experience to diagnose and fix machines.
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 evolving
The career of a maintenance worker in machinery is labeled as "Evolving" because AI tools are increasingly being integrated to help with tasks like spotting issues early and assisting with material handling. However, while AI makes these jobs faster and safer, it doesn't replace the need for skilled human workers who use their senses and experience to diagnose and fix machines.
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
CareerVillage's proprietary model that estimates how resilient each occupation's tasks are to AI automation and augmentation
Microsoft's Working with AI
AI Applicability
Measures how applicable AI tools (like Bing Copilot) are to each occupation based on real usage patterns
Will Robots Take My Job
Automation Resilience
Estimates the probability of automation for each occupation based on research from Oxford University and other academic sources
Althoff & Reichardt
Economic Growth
Measured as "Wage bill" which is a long term projection for average wage × employment. It's the total labor income flowing to an occupation
Low 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
Maint. Workers, Machinery
Updated Quarterly • Last Update: 2/17/2026

What's changing and what's not
In real factories today, maintenance tasks are only partly done by pure machines. Experts note that mechanics still rely on their eyes, ears, and manuals to find problems. For example, the U.S. Bureau of Labor Statistics explains that technicians often “observe mechanical operation” and use computerized tests and vibration analysis to locate faults [1].
However, new AI tools are helping. Some factories use predictive maintenance software and smart sensors to spot issues early. A recent industry report found one plant where AI-powered inspections (like computer-vision checking parts) cut defects by 90% and saved £2 million a year by catching problems before breakdowns [2].
Robots are also assisting with material handling. For instance, the Spanish carmaker SEAT uses small “EffiBOT” robots to bring parts and tools to workers on the line [3], reducing heavy lifting and speeding up supply. These examples show AI helping with routine or hard labor, but not fully replacing the hands-on work.
In fact, many maintenance chores like reassembling equipment or interpreting complex specs remain largely manual. Industry surveys emphasize that experienced workers often “know every sound a machine makes” and can sense trouble in ways a program cannot [2]. In short, AI today augments maintenance crews – making tasks faster or safer – rather than doing all the work by itself.

AI in the real world
How fast factories adopt these AI tools depends on many factors. On one hand, well-known automation and software products for maintenance are commercially available (just like any business software). Companies that buy them often see big benefits.
As one expert noted, the factories getting the most use from AI are those with quick paybacks: some projects now pay for themselves in under a year [2]. In fact, industry surveys show many factories already using AI – for example, 53% of UK manufacturers report using AI on the shop floor [2]. Savings from reduced downtime and better quality (like the £2M in the maintenance example) encourage companies to invest.
On the other hand, costs and workforce issues slow adoption. High-end robots and AI systems still cost a lot up-front, so smaller shops may stick with familiar methods for now. Also, maintenance work varies a lot from one machine to another; unreliable tasks are harder to fully automate.
Labor trends also matter: many maintenance teams have aging, highly experienced workers who are hard to replace [2]. In fact, one maintenance survey found two-thirds of companies see an aging workforce as a top challenge. When skilled people start to retire, firms may turn to AI tools to fill gaps, but they still need human guidance.
Overall, experts emphasize that people will remain essential. AI is seen as a helper, not a replacement. For example, industry leaders describe AI as a “co-pilot” for technicians, giving them data and alerts while letting the human make decisions [4].
In short, maintenance workers who learn to use AI tools can get more done, and companies gain efficiency – but the human skill of the mechanic stays at the center of the job [4] [2].

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Median Wage
$60,500
Jobs (2024)
57,500
Growth (2024-34)
-2.8%
Annual Openings
4,800
Education
High school diploma or equivalent
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
AI-generated estimates of task resilience over the next 3 years
Inventory and requisition machine parts, equipment, and other supplies so that stock can be maintained and replenished.
Reassemble machines after the completion of repair or maintenance work.
Record production, repair, and machine maintenance information.
Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.
Remove hardened material from machines or machine parts, using abrasives, power and hand tools, jackhammers, sledgehammers, or other equipment.
Inspect or test damaged machine parts, and mark defective areas or advise supervisors of repair needs.
Replace, empty, or replenish machine and equipment containers such as gas tanks or boxes.
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.

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