Somewhat Resilient
Last Update: 6/19/2026
AI Resilience Score for Maint. Workers, Machinery:
42.7%
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Med
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
Low
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
Low
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.
Most data sources align, with only minor variation. This is a well-supported result.
Contributing sources
AI Resilience Report forMaintenance Workers, Machinery
$60,500 median salary•4,800 annual openings•SOC Code: 49-9043.00
Maintenance Workers, Machinery are somewhat less resilient to AI impacts than most occupations, according to our analysis of 5 sources.
Machinery maintenance workers land in the "Somewhat Resilient" category because AI is genuinely changing a meaningful part of the job, even though it cannot replace the hands-on work that makes up the core of it. The physical tasks, like dismantling machines, lifting heavy parts, and troubleshooting problems on the shop floor, are extremely difficult for AI to replicate, so those remain firmly in human hands.
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Machinery maintenance workers land in the "Somewhat Resilient" category because AI is genuinely changing a meaningful part of the job, even though it cannot replace the hands-on work that makes up the core of it. The physical tasks, like dismantling machines, lifting heavy parts, and troubleshooting problems on the shop floor, are extremely difficult for AI to replicate, so those remain firmly in human hands.
Read full analysisAnalysis of Current AI Resilience
Maint. Workers, Machinery
Updated Quarterly
How is AI changing Maint. Workers, Machinery jobs?
Good news first: most of what a machinery maintenance worker does with their hands — dismantling machines, hammering off hardened buildup, hoisting parts with cranes — is very hard for AI to replicate, which is why those tasks score only 6–7% on automation. What AI is changing fastest is the "thinking and paperwork" part of the job. Plant Engineering reports that manufacturers are now combining IoT sensors with AI software like IBM Maximo to collect equipment data, proactively identify issues, and tell crews exactly when a part needs servicing [1].
On the SMRP professional forum, reliability engineers describe how AI and smart sensors are increasingly used to monitor machine health and catch faults early [2]. McKinsey calls this approach "rewiring maintenance with gen AI," [3] where chatbots help technicians look up manuals, log repairs, and draft work orders — augmenting the human, not replacing them.
Sources
How fast is AI adoption growing for Maint. Workers, Machinery?
Adoption is moving quickly because the financial case is huge. Deloitte Insights notes that poor maintenance can cut a plant's productive capacity by 5–20%, and unplanned downtime costs manufacturers an estimated $50 billion per year [4]. The World Economic Forum estimates the global "maintenance gap" causes annual economic damage between $1 trillion and $3 trillion and a carbon footprint the size of China's [5], so companies have strong incentives to invest in Industrial AI.
At the same time, demand for skilled humans is rising, not falling. The U.S. Bureau of Labor Statistics projects employment of industrial machinery mechanics and maintenance workers will grow 13 percent from 2024 to 2034 — much faster than average — with about 54,200 openings each year [6]. The takeaway for you: AI is becoming a powerful sidekick that handles record-keeping and predictions, while the hands-on troubleshooting, teamwork, and judgment that keep factories running remain firmly human jobs — and they pay well.
Sources
Will AI replace Maint. Workers, Machinery?
Not entirely. We think AI will take over some tasks, but not the whole job.
Our 42.7% AI Resilience Score reflects a real tension in this field. AI is already changing the "thinking and paperwork" side of maintenance fast. Manufacturers are combining IoT sensors with software to predict when parts will fail and tell crews exactly when to act [1]. Chatbots help technicians look up manuals, log repairs, and draft work orders [3]. That kind of routine cognitive work is shifting to machines.
What stays human is the physical, judgment-heavy core of the job: dismantling equipment, hoisting parts, diagnosing problems in noisy, unpredictable environments. Those hands-on tasks are genuinely hard for AI to replicate. The financial pressure to invest in industrial AI is enormous, since unplanned downtime costs manufacturers an estimated $50 billion per year [4], but that pressure is driving companies to augment workers, not eliminate them.
The honest catch is that long-term demand and earning potential for this role are weaker than the job growth headline suggests, so the economic picture is mixed. Still, the Bureau of Labor Statistics projects about 54,200 openings per year through 2034 [6]. The workers who learn to partner with AI tools will be in the strongest position.
Sources
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Latest AI news for Maint. Workers, Machinery
These articles highlight how AI is transforming the maintenance field, offering maintenance workers valuable tools for success. For instance, Senseye's predictive maintenance technology enables workers to anticipate equipment issues before they occur, enhancing efficiency and reducing downtime. Additionally, the emphasis on training future workers in AI applications ensures that they won't be replaced but rather empowered to leverage new technologies. Embracing AI in maintenance roles can lead to more strategic, data-driven work, making it a resilient career choice in an evolving industry.
The Guide to AI in Field Service Management
www.ibm.com • 3/3/2026
Artificial intelligence (AI) in field service management (FSM) is driving a shift from reactive responses to predictive, data-driven...
AI will infiltrate the industrial workforce in 2026—let’s apply it to training the next generation, not replacing them
fortune.com • 1/15/2026
A silent crisis is shaking the very foundations of modern society. Here's how to turn it into a loud victory.
From reactive to predictive: How AI is transforming maintenance strategies
blogs.opentext.com • 10/1/2025
Discover how predictive maintenance uses IoT, AI, and real-time data to reduce downtime, prevent equipment failures, and improve supply...
Senseye: Predictive Maintenance with AI-Driven Visibility and Insights
www.arcweb.com • 9/4/2025
With generative AI doing the footwork in the background, Maintenance Copilot Senseye creates a high level of visibility and insight into everyday...
The Future of Manufacturing: Top 5 Jobs in an AI-Powered Era
www.ien.com • 7/25/2025
Discover the top 5 cutting-edge jobs reshaping manufacturing in the AI era, from robot wranglers to AI systems integrators driving industry...
More Career Info
Career: Maintenance Workers, Machinery
They keep machines running smoothly by checking, fixing, and cleaning them to prevent breakdowns and ensure everything works safely and efficiently.
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Employment & Wage Data
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
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Collaborate with other workers to repair or move machines, machine parts, or equipment.
2
Remove hardened material from machines or machine parts, using abrasives, power and hand tools, jackhammers, sledgehammers, or other equipment.
3
Lubricate or apply adhesives or other materials to machines, machine parts, or other equipment, according to specified procedures.
4
Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.
5
Reassemble machines after the completion of repair or maintenance work.
6
Transport machine parts, tools, equipment, and other material between work areas and storage, using cranes, hoists, or dollies.
7
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.
