Food Preparation Workers
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
Perform a variety of food preparation duties other than cooking, such as preparing cold foods and shellfish, slicing meat, and brewing coffee or tea.
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 |
|---|---|---|
| Prepare foods or beverages. | 33.0% | High overlap 0.61 |
| Stock supplies or products. | 15.7% | Not observed 0.00 |
| Clean tools, equipment, facilities, or work areas. | 8.6% | Not observed 0.00 |
| Clean workpieces, finished products, or other objects. | 8.1% | Not observed 0.00 |
| Dispose of waste or debris. | 6.6% | Not observed 0.00 |
| Package objects. | 4.5% | Not observed 0.00 |
| Provide food or beverage services. | 4.4% | Not observed 0.00 |
| Move materials, equipment, or supplies. | 4.3% | Not observed 0.00 |
| Measure physical characteristics of materials, products, or equipment. | 4.0% | Moderate 0.42 |
| Communicate with others about operational plans or activities. | 3.9% | High overlap 0.61 |
| Monitor equipment operation. | 3.9% | Not observed 0.00 |
| Distribute materials, supplies, or resources. | 3.0% | 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 prepare foods or beverages; the most important activity AI is not observed doing is stock supplies or products. 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 Prepare foods or beverages, 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 Stock supplies or products.
- 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.
- 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.