π‘ Patternmakers, Wood have a composite risk score of 67/100 (Frey-Osborne probability: 91%, GenAI exposure: 57/100). With 180 workers in the US, this is one of the most AI-vulnerable occupations. Full occupation profile β
π― The Verdict
Likely for many tasks. The role will look very different in 5β10 years.
With 180 workers and a median wage of $53K,patternmakers, wood represent a significant portion of the US workforce. Their GenAI exposure index is 57%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
67/100
Employment
180
Median Wage
$53K
GenAI Exposure
57%
β οΈ Top Risk Factors
Smart factory scheduling and production optimization
Industrial robotics replacing manual assembly tasks
AI quality inspection via computer vision systems
Automated CNC programming and machine operation
π‘οΈ Tasks AI Can't Easily Replace
Coordinating workflow across diverse production teams
Setup and calibration of custom production runs
Troubleshooting complex equipment malfunctions
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Engineers
59% skills overlap Β· $106K median wage
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors
69% skills overlap Β· $62K median wage
Fabric and Apparel Patternmakers
83% skills overlap Β· $68K median wage
β Frequently Asked Questions
Will AI completely replace patternmakers, wood?
Likely for many tasks. The role will look very different in 5β10 years.
What is the AI risk score for patternmakers, wood?
Patternmakers, Wood have a composite AI automation risk score of 67 out of 100, classified as "High Risk".
How many patternmakers, wood are there in the US?
There are approximately 180 patternmakers, wood employed in the United States.
What do patternmakers, wood earn?
The median annual wage for patternmakers, wood is $53K.
What skills should patternmakers, wood develop?
Focus on tasks AI can't easily replicate: coordinating workflow across diverse production teams, setup and calibration of custom production runs, troubleshooting complex equipment malfunctions. These human-centric skills will become more valuable as routine tasks are automated.