Somewhat Resilient
Last Update: 7/31/2026
AI Resilience Score for Maint. Workers, Machinery:
44.9%
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
$60,850 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 6 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.
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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.
AI is already reshaping the "thinking and paperwork" side of machinery maintenance. Manufacturers are combining IoT sensors with software to predict equipment failures before they happen, and chatbots now help technicians look up manuals and log repairs [3]. Companies have strong financial reasons to push this fast: unplanned downtime costs manufacturers an estimated $50 billion per year [4], and the global maintenance gap causes economic damage between $1 trillion and $3 trillion annually [5].
What AI cannot easily replace is the hands-on work: dismantling machines, hoisting parts, diagnosing a problem in a noisy, greasy environment with incomplete information. Those physical and judgment-heavy tasks are genuinely hard to automate. That said, our 44.9% AI Resilience Score reflects real pressure on this career, particularly around long-term demand and earning flexibility, so workers should expect the role to keep evolving.
The practical advice: treat AI tools as a sidekick worth learning, not a threat to fear. The workers who adapt by building skills in predictive maintenance technology and digital diagnostics will be better positioned than those who ignore the shift entirely.
Sources
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Latest AI news for Maint. Workers, Machinery
These articles highlight the transformative role of AI in the maintenance field, emphasizing the shift towards predictive maintenance. For instance, Senseye showcases how AI can provide real-time insights, enhancing decision-making for maintenance workers. Additionally, IBM illustrates how AI allows for proactive rather than reactive maintenance, potentially saving companies significant costs associated with downtime. Embracing these advancements can bolster your career resilience in a rapidly evolving industry, positioning you as a key player in the future of machinery maintenance.
The Role of AI in Predictive Maintenance
www.ibm.com • 1/28/2026
Predictive maintenance (PdM) represents a paradigm shift. Instead of relying on averages or guesswork, AI-based predictive maintenance uses...
Don’t Sleep on the Factory Floor: The Overlooked Impacts of AI on Manufacturing
www.commerce.nc.gov • 1/6/2026
Lost in the news and hype of Artificial Intelligence's impact on office work is the current and potential effects AI and machine learning...
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...
To Reduce Equipment Downtime, Manufacturers Turn to AI Predictive Maintenance Tools
biztechmagazine.com • 3/3/2025
Equipment malfunctions are costly and disruptive. When equipment goes down and work stops, the hourly cost to a business ranges from $36,000...
Rewiring maintenance with gen AI
www.mckinsey.com • 2/6/2025
Modern machines are getting harder to maintain. Extra features, multiple sensors, advanced control systems, and sophisticated software all...
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
$60,850
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.
