Oral Expression

Occupations that rely most on Oral Expression

Ranked by O*NET importance to the occupation (1–5). Bars are sized against the 1–5 scale; the level column is what depth of the ability the job needs (0–7).

Showing the top 40 of 852 occupations where this is important.

How AI is used by roles that need Oral Expression

This ability is not itself "being automated" — but we can look at the roles for which O*NET rates it important and ask how those people actually use AI. This rolls the Anthropic Economic Index per-role signal up across those roles (importance-weighted). 58.8% of the 852 roles where this is important carry observed AI-usage data (501 roles).

Across those roles, 46.6% of AI conversations are people working with AI and 32.2% hand a task to AI , with an average autonomy of 3.57 / 5.

Roles behind this signal

The roles where this ability is most important and that also 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. Shares are of observed conversations, weighted by how important this ability is to each role; some conversations are left unclassified by Anthropic's taxonomy, so shares need not sum to 100.

Industries that concentrate this

Where Oral Expression matters most across the economy. Employment reach is the share of an industry's workers in occupations that significantly rely on Oral Expression (O*NET importance ≥ 3 of 5). 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 86.2% of workers are in occupations that significantly rely on Oral Expression (measured across 67 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.

Related abilities, skills & knowledge

Capabilities required by many of the same occupations — a measure of which skills, knowledge and abilities tend to travel together, not a judgment of similarity.

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

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-oral-expression,
  title  = {Oral Expression},
  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). Accessed June 7, 2026},
  url    = {https://singulariki.com/abilities/oral-expression}
}