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The AI Resilience Report helps you understand how AI is likely to impact your current or future career. Drawing on data from over 1,500 occupations, it provides a clear snapshot to support informed career decisions.
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Last Update: 4/23/2026
Your role’s AI Resilience Score is
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%).
Med
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.
There are a reasonable number of sources for this result, but there is some disagreement between them.
Contributing sources
Helpers--Installation, Maintenance, and Repair Workers are somewhat less resilient to AI impacts than most occupations, according to our analysis of 5 sources.
This career is labeled as "Somewhat Resilient" because while AI and robots can automate some repetitive tasks like moving and lifting, many complex duties still need human skills. Tasks like repairing machines and making quick decisions on the spot are challenging for robots and rely on human judgment and dexterity.
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 somewhat resilient
This career is labeled as "Somewhat Resilient" because while AI and robots can automate some repetitive tasks like moving and lifting, many complex duties still need human skills. Tasks like repairing machines and making quick decisions on the spot are challenging for robots and rely on human judgment and dexterity.
Read full analysisAnalysis of Current AI Resilience
Helpers - Inst/Maint/Repair
Updated Quarterly • Last Update: 2/17/2026

In many shops and warehouses, some helper tasks are already being done with smart machines or AI support. For example, big warehouses use robot carts that automatically carry bins of parts and tools to workers [1]. In factories, robotic arms and forklifts can stack or move heavy equipment, even loading and unloading trucks and placing goods on pallets [2].
These machines handle repetitive moving and lifting, while people oversee them. Companies also use sensors and AI software to watch machines (called “predictive maintenance”): for instance, equipment can have vibration or temperature sensors that alert a human technician before something breaks.
At the same time, many helper duties remain too complex for robots. Tasks like taking apart a broken machine for repair still rely on skilled helpers. (Researchers have built test robots that can disassemble machines on assembly lines [3], but that technology is mostly in labs.) Likewise, installing or positioning odd-shaped equipment needs human judgment. Experts point out that robots still “struggle” with picking the right item from a mixed pile – something almost any person can do easily [2].
In short, routine moving and monitoring tasks are increasingly automated, but hands-on repair, assembly, and decision-making are still done by people.

Adopting AI and robots depends on cost and benefit. In favor of automation, machines can work nonstop and be very efficient. For example, Amazon tested a warehouse robot that unloads twice as many packages per hour as a person and “doesn’t call in sick” [2].
With about 1.8 million U.S. workers in these roles [2], companies with tight labor markets may welcome robots to help with shortages. On the other hand, installation and repair work is unpredictable and often requires flexibility. As one report noted, even advanced robots can’t yet match humans at simple appraisals like grabbing the right part from a bin [2].
Because of these challenges – plus the high price of equipment and training – most firms move slowly. The U.S. Bureau of Labor Statistics observes that helper jobs themselves haven’t really shrunk due to automation (any changes were mostly technical coding shifts) [4].
Overall, AI can make helper work more efficient (by speeding up heavy lifting or giving maintenance alerts), but human skills remain crucial. Young people entering this field can learn to work with these new tools. Technology may change how the job is done, but it still needs the flexibility, problem-solving, and safety judgment that people provide [2] [4].

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They assist skilled workers by carrying tools, holding materials, and cleaning up, helping to fix or install things like machines or equipment.
Median Wage
$38,860
Jobs (2024)
98,700
Growth (2024-34)
+2.3%
Annual Openings
11,800
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
Hold or supply tools, parts, equipment, and supplies for other workers.
Disassemble broken or defective equipment to facilitate repair and reassemble equipment when repairs are complete.
Position vehicles, machinery, equipment, physical structures, and other objects for assembly or installation, using hand tools, power tools, and moving equipment.
Install or replace machinery, equipment, and new or replacement parts and instruments, using hand or power tools.
Assemble and maintain physical structures, using hand or power tools.
Prepare work stations for use by mechanics and repairers.
Adjust, connect, or disconnect wiring, piping, tubing, and other parts, using hand or power tools.
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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