Label making software

Example software & tools

Ranked by how many occupations list each product. Each number is an occupation count — a job is counted once per product — so the product rows overlap and do not sum to the category total.

Occupations that use Label making software

• Histology Technicians
• Histotechnologists
• Inspectors, Testers, Sorters, Samplers, and Weighers
• Naturopathic Physicians
• Packaging and Filling Machine Operators and Tenders
• Pharmacists
• Pharmacy Technicians
• Print Binding and Finishing Workers
• Shipping, Receiving, and Inventory Clerks
• Transportation, Storage, and Distribution Managers
• Veterinary Assistants and Laboratory Animal Caretakers

How AI is used by roles that use Label making software

A software category is not itself "being automated" — but we can look at the roles that report using Label making software and ask how those people actually use AI. This rolls the Anthropic Economic Index per-role signal up across those roles, weighted by how much observed AI activity each one has. 45.5% of the 11 roles that use this category carry observed AI-usage data (5 roles).

Across those roles, 62.8% of AI conversations are people working with AI and 25.7% hand a task to AI , with an average autonomy of 3.43 / 5.

Roles behind this signal

The roles using this category that have the most AEI data. "Works with AI" is the role's share of conversations that augment rather than automate.

Source: Anthropic Economic Index (2026-01-15-v4-plus-2025-03-27-v2) over a sample of Claude.ai Free and Pro conversations — not all AI tools and not the whole workforce. Roles list software categories in O*NET; this does not mean AI is used inside Label making software, only that people in those roles use AI. Some conversations are left unclassified, so shares need not sum to 100.

Industries that concentrate this

Where Label making software matters most across the economy. Employment reach is the share of an industry's workers in occupations that significantly use Label making software (O*NET importance ≥ 3 of 5, or report using the tool category). Concentration compares that reach to the national average industry, so a value above 1× means the requirement is more pervasive here than across the economy as a whole.

Nationally, about 2.1% of workers are in occupations that significantly use Label making software (measured across 56 industries).

Sectors with the most such workers

Industries where it is most concentrated

Reach is a measure of how widespread a requirement is across an industry's workforce, not how intensively any individual uses it. Sector worker counts come from BLS OEWS employment; the significance threshold and tool use come from O*NET. Industries shown by concentration are filtered to a real worker base so a tiny specialty cannot top the list on rounding.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
• Census NAICS 2022 U.S. Census Bureau
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
• AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic

Data compiled June 3, 2026. Figures are estimates, not advice.

@misc{singulariki-label-making-software,
  title  = {Label making software},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026},
  url    = {https://singulariki.com/tools/label-making-software}
}