Business

ai-operating-economics

Helps evaluate whether AI-enabled workflows should be adopted, scaled, constrained, redesigned, or retired using outcome evidence, countermetrics, full operating costs, uncertainty, and governance.

★ 88 GitHub Stars
95 Score de qualité
7 Fichiers

About this Skill

Use when deciding whether an AI-enabled workflow should be adopted, scaled, constrained, redesigned, or retired, and the decision must connect business outcomes, worker or user effects, quality guardrails, full operating cost, telemetry, uncertainty, and accountable governance. Do not use for a standalone financial model, infrastructure cost calculation, agent evaluation design, runtime operations, or general AI governance; route those details to the neighboring specialist skills.

Cas d’usage

  • Build an evidence-backed business case for an AI workflow
  • Review AI pilot results and scale decisions
  • Assess productivity, savings, and transformation claims
  • Design post-launch value-realization reviews
  • Prepare executive or portfolio decisions about AI interventions

Fonctionnalités

  • Defines workflow-specific value hypotheses and decision boundaries
  • Maps primary outcomes, leading indicators, countermetrics, and guardrails
  • Separates marginal and fully loaded operating costs
  • Supports evidence classification, uncertainty analysis, and segmentation
  • Provides initiative evidence and economics review templates

Agents compatibles

Not specified

Skill files

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