Floristen
Speisenzubereitung und ServiceKI-Exposition
- Datenquelle: BLSVeröffentlicht: 2026-08
Mittel· relativ
NiedrigVier relative BänderSehr hochWert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Vier relative Bänder (Niedrig / Mittel / Hoch / Sehr hoch)
831 detaillierte Berufe in der BLS-Beschäftigungsprojektionstabelle. Der Wert wird je Code der National Employment Matrix (NEM) vergeben, daher erhalten Berufe mit demselben NEM-Code dasselbe Band
- Datenquelle: AnthropicVeröffentlicht: 2026-03
0.000
0.000Spannweite der hier geführten Werte0.745Skala, Bezugsmenge und Quelle
Index der beobachteten Exposition, 0–1 wie veröffentlicht
Auf O*NET-Aufgaben abgebildet
- Datenquelle: ILOVeröffentlicht: 2025
0.14
0.09Spannweite der hier geführten Werte0.70Wert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Index der Exposition gegenüber generativer KI, 0–1 wie veröffentlicht
ISCO-08-Berufsgattung — alle Berufe mit demselben Code erhalten diesen Wert
Von dieser Website berechnet, nicht von der IAO veröffentlicht: Von den 1.012 Berufen, die diese Website mit dem IAO-Datensatz verknüpft, erreichen oder übertreffen 95% diesen Wert.
Welche Art von Wert diese Quelle veröffentlicht
Die Kategorie von BLS ist ein relativer Rang und kein absolutes Niveau, und sie ist keine eigene Messung: Sie fasst die Perzentilränge des Berufs aus mehreren veröffentlichten Studien in vier Bänder zusammen. Sie ist weder eine Beschäftigungs- oder Lohnprognose noch eine Einführungswahrscheinlichkeit und unterscheidet nicht zwischen Automatisierung und Augmentierung.
Task-level exposure
Nur exponierte Aufgaben| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Plan arrangement according to client's requirements, using knowledge of design and properties of materials, or select appropriate standard design pattern.27-1023 | 0.020066.7 | 0.0100100.0 |
Conduct classes or demonstrations, or train other workers.27-1023 | 0.010033.3 | 0.00000.0 |
Confer with clients regarding price and type of arrangement desired and the date, time, and place of delivery. | —0 | 0.00000.0 |
| Not observed on any surface — 12 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. | ||
Water plants, and cut, condition, and clean flowers and foliage for storage. | —0 | —0 |
Select flora and foliage for arrangements, working with numerous combinations to synthesize and develop new creations. | —0 | —0 |
Order and purchase flowers and supplies from wholesalers and growers. | —0 | —0 |
Wrap and price completed arrangements. | —0 | —0 |
Trim material and arrange bouquets, wreaths, terrariums, and other items, using trimmers, shapers, wire, pins, floral tape, foam, and other materials. | —0 | —0 |
Perform office and retail service duties, such as keeping financial records, serving customers, answering telephones, selling giftware items, and receiving payment. | —0 | —0 |
Inform customers about the care, maintenance, and handling of various flowers and foliage, indoor plants, and other items. | —0 | —0 |
Decorate, or supervise the decoration of, buildings, halls, churches, or other facilities for parties, weddings and other occasions. | —0 | —0 |
Perform general cleaning duties in the store to ensure the shop is clean and tidy. | —0 | —0 |
Unpack stock as it comes into the shop. | —0 | —0 |
Create and change in-store and window displays, designs, and looks to enhance a shop's image. | —0 | —0 |
Deliver arrangements to customers, or oversee employees responsible for deliveries. | —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 |
|---|---|
Inform customers about the care, maintenance, and handling of various flowers and foliage, indoor plants, and other items.O*NET Task ID 1727 | 1.0 |
Select flora and foliage for arrangements, working with numerous combinations to synthesize and develop new creations.O*NET Task ID 1722 | 0.5 |
Order and purchase flowers and supplies from wholesalers and growers.O*NET Task ID 1723 | 0.5 |
Perform office and retail service duties, such as keeping financial records, serving customers, answering telephones, selling giftware items, and receiving payment.O*NET Task ID 1726 | 0.5 |
Create and change in-store and window displays, designs, and looks to enhance a shop's image.O*NET Task ID 1731 | 0.5 |
Confer with clients regarding price and type of arrangement desired and the date, time, and place of delivery.O*NET Task ID 1719 | 0.0 |
Plan arrangement according to client's requirements, using knowledge of design and properties of materials, or select appropriate standard design pattern.O*NET Task ID 1720 | 0.0 |
Water plants, and cut, condition, and clean flowers and foliage for storage.O*NET Task ID 1721 | 0.0 |
Wrap and price completed arrangements.O*NET Task ID 1724 | 0.0 |
Trim material and arrange bouquets, wreaths, terrariums, and other items, using trimmers, shapers, wire, pins, floral tape, foam, and other materials.O*NET Task ID 1725 | 0.0 |
Decorate, or supervise the decoration of, buildings, halls, churches, or other facilities for parties, weddings and other occasions.O*NET Task ID 1728 | 0.0 |
Perform general cleaning duties in the store to ensure the shop is clean and tidy.O*NET Task ID 1729 | 0.0 |
Unpack stock as it comes into the shop.O*NET Task ID 1730 | 0.0 |
Conduct classes or demonstrations, or train other workers.O*NET Task ID 1732 | 0.0 |
Grow flowers for use in arrangements or for sale in shop.O*NET Task ID 1733 | 0.0 |
Deliver arrangements to customers, or oversee employees responsible for deliveries.O*NET Task ID 20751 | 0.0 |
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