Patternmakers, Metal and Plastic
AI touches this job. It does not define it.
AI meaningfully touches parts of this work, yet the majority of its activities remain outside observed AI usage. That's balanced exposure: real efficiency gains are available without wholesale overlap.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Lay out, machine, fit, and assemble castings and parts to metal or plastic foundry patterns, core boxes, or match plates.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Measure physical characteristics of materials, products, or equipment. | 16.6% | Moderate 0.42 |
| Operate cutting or grinding equipment. | 16.1% | Not observed 0.00 |
| Assemble products or work aids. | 11.1% | Not observed 0.00 |
| Program computer systems or production equipment. | 7.7% | High overlap 0.68 |
| Design materials or devices. | 7.2% | Emerging 0.31 |
| Repair workpieces or products. | 6.5% | Not observed 0.00 |
| Read documents or materials to inform work processes. | 6.4% | Moderate 0.55 |
| Plan work activities. | 6.4% | Moderate 0.60 |
| Join parts using soldering, welding, or brazing techniques. | 6.1% | Not observed 0.00 |
| Smooth surfaces of objects or equipment. | 5.5% | Not observed 0.00 |
| Clean workpieces, finished products, or other objects. | 5.5% | Not observed 0.00 |
| Mark materials or objects for identification. | 5.0% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is measure physical characteristics of materials, products, or equipment; the most important activity AI is not observed doing is operate cutting or grinding equipment. The plan below is built around exactly that split.
Balanced exposure: take the gains, deepen the moat.
Next 30 days
- Harvest the easy wins. Parts of this role, led by Measure physical characteristics of materials, products, or equipment, are already AI-assistable. Claim those hours back this month.
- Map your own mix. Your split may differ from the occupation average. Run the six-question personalization above and read your range, not the headline.
Next 90 days
- Invest saved time in the human core. The majority of this occupation's activities show no observed AI usage. Deepen exactly those, starting with Operate cutting or grinding equipment.
- Stay current, lightly. A monthly hour trying new tools on your real tasks beats panic-learning later.
The longer game
- Own a niche. Moderate exposure means AI will not define this occupation, but specialists who pair domain depth with tool fluency will out-earn generalists in it.
- Watch the boundary activities. If the 'Emerging' rows in your table above turn 'High' in a future data update, revisit the plan. No countdown clocks, just recheck.
Where to put your learning time
AI reaches into parts of this work, above all the activity measure physical characteristics of materials, products, or equipment, while most of this occupation's measured activities sit outside it. That makes augmentation the realistic path: learn the tools well enough to direct them through the exposed slice, then reinvest the recovered hours in the lower-overlap half of your week, starting with the activity operate cutting or grinding equipment.
- Full task, skill and pay profile for this occupation on O*NET OnLine Free
- Browse role-relevant courses on Coursera Partner link
Most Coursera courses can be audited free; payment covers graded work and certificates. The partner link earns this site a commission if you subscribe, at no extra cost to you, and it never changes a score, a ranking or a plan here.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.