From AI Answers to Auditable Decisions: An Evidence Framework for Business Analysis

A business recommendation can sound convincing while obscuring the evidence, assumptions, and disagreements behind it. Citations help readers locate information, but they do not establish whether a proposed decision follows from that information. This paper develops a conceptual evidence framework for business analysis. It separates source observations, interpretations, assumptions, recommendations, and accountable decisions. Versioned records connect each material claim to supporting passages, conflicting evidence, and the person responsible for review. The proposed architecture combines a controlled evidence registry, claim assessment, and a decision record. A fictional product prioritization example demonstrates how limited interview evidence can support a bounded pilot without supporting a general rollout. The evaluation plan tests evidence accuracy, omitted objections, reviewer effort, and the ability to reconstruct decisions. Auditability requires more than attaching references to an answer. Organizations need to preserve how evidence was interpreted, which uncertainties remained, and who accepted them. The framework is a design proposal; no deployment results are reported.

Artificial Intelligence