AI Delegation Index

AI Delegation Index

What work could you hand off to an AI agent?

Computer Numerically Controlled Tool Programmers

Moderate

AI agents could support several recurring tasks in this job, while people continue to lead decisions and handle exceptions.

Where agents can help most

  1. 1

    Turn part specs into a CNC program

    Use an agent to read job orders, drawings, blueprints, and design data, then draft the dimensions, tool choices, speeds, feed rates, and a controller-ready program file.

  2. 2

    Fix program mismatches against blueprints

    Use an agent to compare the program printout or tape listing with the original blueprint, mark the coordinate or tool-call differences, and draft a corrected version for retesting.

  3. 3

    Test-run and correct a machining program

    Use an agent to organize trial-run notes, simulation results, and any program edits, then draft a corrected machining program and a retest checklist.

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O*NET-SOC 51-9162.00 · #208 of 923

Result context

How to read this result

Develop programs to control machining or processing of materials by automatic machine tools, equipment, or systems. May also set up, operate, or maintain equipment.

National position
#208 of 923 occupations
Top 23% of occupations
Overall AI delegation potential
Moderate
Potential of score-contributing workflows
Strong · 81/100
Meaningful work covered
36%

The overall rating combines how useful the best agent workflows are with how much of the occupation they address. It is not an estimate of job automation or replacement.

Recommended agent uses

3 workflows you could delegate to AI

1

Turn part specs into a CNC program

How you could use an agent

Use an agent to read job orders, drawings, blueprints, and design data, then draft the dimensions, tool choices, speeds, feed rates, and a controller-ready program file. You get a program package that is easier to compare with the print and load into the machine environment.

Where you stay involved

You check the coordinates, tool path, and setup details against the print, then decide whether the program is ready for use or needs more revision.

Review level: High

2

Fix program mismatches against blueprints

How you could use an agent

Use an agent to compare the program printout or tape listing with the original blueprint, mark the coordinate or tool-call differences, and draft a corrected version for retesting. You get a clear change list and a revised file to check before it goes back to the machine.

Where you stay involved

You decide whether the mismatch is real, approve the correction, and stop the release if the drawing revision or setup choice is unclear.

Review level: High

3

Test-run and correct a machining program

How you could use an agent

Use an agent to organize trial-run notes, simulation results, and any program edits, then draft a corrected machining program and a retest checklist. You get a cleaner review packet that shows what changed and whether the revised run still needs attention.

Where you stay involved

You watch the trial or simulation results, decide whether the revised code is acceptable, and stop the job if the machine looks unsafe or the part definition changes.

Review level: High

Supporting analysis

Why this occupation's AI delegation potential is Moderate

Underlying methodology score

71 / 100

This technical score determines the qualitative rating; it is not an estimate of the share of the occupation that can be automated.

Importance & frequency81
AI capability82
Digital actionability86
End-to-end leverage81
Safety & reversibility75
Meaningful-work coverage
36%
Physical-work modifier
Limited
Safety modifier
Limited
Qualitative judgment
No material constraint
O*NET task evidence
16 tasks

O*NET 31.0 · methodology 3.3.0. Every workflow passes an action-level physical-execution and protected human-and-veterinary clinical-action gate. Documentation workflows must own a complete digital loop and use digital task evidence only; support-only workflows are disclosed separately and excluded from scoring. Artistic, editorial, normative, and policy-dependent work receives a transparent human-judgment constraint. National ranking within 923 scored O*NET occupations under methodology 3.3.0.

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