57
/100

SOC 47-5022

Excavating and Loading Machine and Dragline Operators, Surface Mining

ElevatedFrey/Osborne: 85.0%

Risk Score

โš ๏ธ

57/100

Elevated

US Employment

๐Ÿ‘ฅ

34,210

Total workers

Median Wage

๐Ÿ’ฐ

$53K

$40K โ€“ $81K

Projected Growth

๐Ÿ“ˆ

-0.4%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

29/100

Low exposure

How we calculate these numbers โ†’

๐Ÿ’ก Excavating and Loading Machine and Dragline Operators, Surface Mining face a risk score of 57/100 โ€” 13 points above the national average of 44. With only 29/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $53K ($6K above the national median). The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 57/100, Excavating and Loading Machine and Dragline Operators, Surface Mining faces moderate automation pressure. While tasks like bim-integrated automated progress tracking are increasingly handled by AI, the role retains significant human elements. The 34,210 workers in this occupation should focus on strengthening skills in client-facing consultation on custom project needs and fine motor craftsmanship in custom installations to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Excavating and Loading Machine and Dragline Operators, Surface Mining?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

BIM-integrated automated progress tracking

2

3D printing of building components

3

Robotic bricklaying and prefabrication automation

4

Drone-based site surveying and inspection

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Client-facing consultation on custom project needs

โœ“

Fine motor craftsmanship in custom installations

โœ“

Navigating unpredictable and unstructured job sites

โœ“

Real-time safety judgment in hazardous conditions

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

Move levers, depress foot pedals, and turn dials to operate power machinery, such as power shovels, stripping shovels, scraper loaders, or backhoes.

โ€ข

Set up or inspect equipment prior to operation.

โ€ข

Become familiar with digging plans, machine capabilities and limitations, and efficient and safe digging procedures in a given application.

โ€ข

Observe hand signals, grade stakes, or other markings when operating machines so that work can be performed to specifications.

โ€ข

Operate machinery to perform activities such as backfilling excavations, vibrating or breaking rock or concrete, or making winter roads.

๐Ÿ’ป Technology Skills

โ€ข

Electronic mail software

โ€ข

Industrial control software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Presentation software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Mechanical

โ€ข

Building and Construction

โ€ข

Public Safety and Security

โ€ข

English Language

โ€ข

Mathematics

๐Ÿ“Š vs National Average

Median Wage$53K
+$6K

National avg: $46K

Risk Score57/100
+13

National avg: 44/100

GenAI Exposure29/100
-9

National avg: 38/100

Projected Growth-0.4%
-4.1%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K59%
Extraction Workers29$56K71%
Electricians32$62K80%