Computer Numerically Controlled Tool Programmers: daily tasks & AI impact
This page breaks Computer Numerically Controlled Tool Programmers into the top 15 daily O*NET tasks and scores each for Generative AI automation probability. Those task scores roll up to an overall vulnerability index of 66/100 (Moderate automation risk). Struck-through rows are ≥80% automatable; highlighted safer rows are under 30% or primarily physical.
High-automation exposure
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Durable human work
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.
| Task | Importance | AI probability | Nature |
|---|---|---|---|
| Determine the sequence of machine operations, and select the proper cutting tools needed to machine workpieces into the desired shapes. Core | 4.56 | — | — |
| Analyze job orders, drawings, blueprints, specifications, printed circuit board pattern films, and design data to calculate dimensions, tool selection, machine speeds, and feed rates. Core | 4.46 | — | — |
| Observe machines on trial runs or conduct computer simulations to ensure that programs and machinery will function properly and produce items that meet specifications. Core | 4.39 | — | — |
| Write programs in the language of a machine's controller and store programs on media, such as punch tapes, magnetic tapes, or disks. Core | 4.36 | — | — |
| Determine reference points, machine cutting paths, or hole locations, and compute angular and linear dimensions, radii, and curvatures. Core | 4.33 | — | — |
| Enter computer commands to store or retrieve parts patterns, graphic displays, or programs that transfer data to other media. Core | 4.22 | — | — |
| Revise programs or tapes to eliminate errors, and retest programs to check that problems have been solved. Core | 4.19 | — | — |
| Modify existing programs to enhance efficiency. Core | 4.06 | — | — |
| Enter coordinates of hole locations into program memories by depressing pedals or buttons of programmers. Core | 3.95 | — | — |
| Sort shop orders into groups to maximize materials utilization and minimize machine setup time. Core | 3.82 | — | — |
| Compare encoded tapes or computer printouts with original part specifications and blueprints to verify accuracy of instructions. Core | 3.81 | — | — |
| Prepare geometric layouts from graphic displays, using computer-assisted drafting software or drafting instruments and graph paper. Core | 3.78 | — | — |
| Perform preventative maintenance or minor repairs on machines. Core | 3.74 | — | — |
| Order tooling for jobs. Core | 3.56 | — | — |
| Write instruction sheets and cutter lists for a machine's controller to guide setup and encode numerical control tapes. Supplemental | 3.55 | — | — |
What remains human
No tasks currently fall under the safe threshold for this occupation.
FAQ
Which Computer Numerically Controlled Tool Programmers tasks are most exposed to AI?
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Which Computer Numerically Controlled Tool Programmers tasks are safest from Generative AI?
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
How should I read the Computer Numerically Controlled Tool Programmers task table?
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.