自動車配車係
輸送・資材運搬AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.226
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.50
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは17%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
露出のある作業のみ| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Confer with customers or supervising personnel to address questions, problems, or requests for service or equipment.43-5032 | 0.0100100.0 | 0.0100100.0 |
Record and maintain files or records of customer requests, work or services performed, charges, expenses, inventory, or other dispatch information.43-5032 | 0.00000.0 | 0.00000.0 |
Relay work orders, messages, or information to or from work crews, supervisors, or field inspectors, using telephones or two-way radios. | —0 | 0.00000.0 |
Determine types or amounts of equipment, vehicles, materials, or personnel required, according to work orders or specifications. | —0 | 0.00000.0 |
| Not observed on any surface — 8 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Schedule or dispatch workers, work crews, equipment, or service vehicles to appropriate locations, according to customer requests, specifications, or needs, using radios or telephones. | —0 | —0 |
Arrange for necessary repairs to restore service and schedules. | —0 | —0 |
Prepare daily work and run schedules. | —0 | —0 |
Receive or prepare work orders. | —0 | —0 |
Monitor personnel or equipment locations and utilization to coordinate service and schedules. | —0 | —0 |
Advise personnel about traffic problems, such as construction areas, accidents, congestion, weather conditions, or other hazards. | —0 | —0 |
Oversee all communications within specifically assigned territories. | —0 | —0 |
Order supplies or equipment and issue them to personnel. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Relay work orders, messages, or information to or from work crews, supervisors, or field inspectors, using telephones or two-way radios.O*NET Task ID 2725 | 1.0 |
Receive or prepare work orders.O*NET Task ID 2728 | 1.0 |
Record and maintain files or records of customer requests, work or services performed, charges, expenses, inventory, or other dispatch information.O*NET Task ID 2731 | 1.0 |
Determine types or amounts of equipment, vehicles, materials, or personnel required, according to work orders or specifications.O*NET Task ID 2732 | 1.0 |
Schedule or dispatch workers, work crews, equipment, or service vehicles to appropriate locations, according to customer requests, specifications, or needs, using radios or telephones.O*NET Task ID 2723 | 0.5 |
Arrange for necessary repairs to restore service and schedules.O*NET Task ID 2724 | 0.5 |
Confer with customers or supervising personnel to address questions, problems, or requests for service or equipment.O*NET Task ID 2726 | 0.5 |
Prepare daily work and run schedules.O*NET Task ID 2727 | 0.5 |
Oversee all communications within specifically assigned territories.O*NET Task ID 2729 | 0.5 |
Monitor personnel or equipment locations and utilization to coordinate service and schedules.O*NET Task ID 2730 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page