Evidence-Based Validation of AI-Generated Enterprise Decisions

Abstract - Enterprise recommendations require evidence that is sufficient for a particular action, not merely factual sentences. A fully supported answer can omit an approval, rely on a superseded policy, or apply a rule to the wrong population. This article develops a context-indexed evidence-obligation ledger: a domain-approved set of mandatory premises exists independently of generated text, and every premise must carry applicable, current support before evidence acceptance. The ledger exposes omitted obligations, records unresolved contradictions, and invalidates acceptance when dependent sources or context change. The contribution is a decision-completeness representation and its lifecycle semantics; it is not a new entailment algorithm or constraint language. A deterministic feasibility demonstration matched authored routing expectations in 36 cases spanning 12 scenario types. An output-support-only proxy accepted 24 cases that the authored contract required to be reviewed. These are conformance findings with preassigned support labels, not estimates of LLM accuracy or comparative enterprise effectiveness. An experiment diagram, protocol, complexity analysis, and reproducibility package define the remaining empirical validation.