Analyze forecasting data to improve business decisions.

Analyze forecasting data to improve business decisions. is a detailed work activity in O*NET — a concrete unit of work shared across 9 occupations and seen in 10 occupation-specific tasks. It rolls up into the broader work activity Analyze data to improve operations. in Analyzing Data or Information .

Detailed work activities are the most granular shared layer in O*NET's work-activity hierarchy (Generalized → Intermediate → Detailed → occupation-specific task). The figures below describe how this activity shows up across the economy and what independent studies measure about AI and this kind of work — not a prediction that the work will be automated.

AI exposure

Of the 10 tasks under this activity that the OpenAI / Eloundou “GPTs are GPTs” study rated, 10 (100%) are flagged as directly exposed to language models (E1) or exposed via model-powered tools (E2).

The Anthropic Economic Index observes real AI use on 3 of these tasks, with a mean mapped-usage share of 0.014% per task.

Exposure estimates overlap with model capabilities — whether a model could speed the task up — not whether the work will be done by software. Observed AI use is augmentation and assistance today, not jobs replaced.

Member tasks

Occupation-specific tasks O*NET maps to this detailed work activity, most important first.

• Perform or evaluate research, such as detailed company or industry analyses, to inform financial forecasting, decision making, or valuation. · Investment Fund Managers · importance 4.4 · exposure with tools
• Develop or analyze information to assess the current or future financial status of firms. · Financial Managers · importance 4.1 · exposure with tools
• Collect and analyze survey data, regulatory information, and data on demographic and employment trends to forecast enrollment patterns and curriculum change needs. · Education Administrators, Kindergarten through Secondary · importance 3.9 · exposure with tools
• Analyze retail data to identify current or emerging trends in theft or fraud. · Loss Prevention Managers · importance 3.8 · exposure with tools
• Analyze information on property values, taxes, zoning, population growth, and traffic volume and patterns to determine if properties should be acquired. · Property, Real Estate, and Community Association Managers · importance 3.8 · exposure with tools
• Evaluate construction methods and determine cost-effectiveness of plans, using computer models. · Construction Managers · importance 3.8 · exposure with tools
• Collect and analyze survey data, regulatory information, and demographic and employment trends to forecast enrollment patterns and the need for curriculum changes. · Education and Childcare Administrators, Preschool and Daycare · importance 3.6 · exposure with tools
• Use sales forecasting or strategic planning to ensure the sale and profitability of products, lines, or services, analyzing business developments and monitoring market trends. · Marketing Managers · importance 3.6 · exposure with tools
• Analyze marketing or sales trends to forecast future conditions. · Advertising and Promotions Managers · exposure with tools
• Review invoices, work orders, consumption reports, or demand forecasts to estimate peak performance periods and to issue work assignments. · Transportation, Storage, and Distribution Managers · exposure with tools

Occupations that perform this

• Investment Fund Managers
• Education Administrators, Kindergarten through Secondary
• Loss Prevention Managers
• Construction Managers
• Property, Real Estate, and Community Association Managers
• Education and Childcare Administrators, Preschool and Daycare
• Marketing Managers
• Advertising and Promotions Managers
• Transportation, Storage, and Distribution Managers

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
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

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

@misc{singulariki-analyze-forecasting-data-to-improve-business-decisions,
  title  = {Analyze forecasting data to improve business decisions.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/detailed-activities/analyze-forecasting-data-to-improve-business-decisions}
}