This paper documents a systematic infrastructure-driven evidence gap that affects brand performance at the AI purchase recommendation stage. Building on the four-cause diagnostic framework introduced in WP-2026-04 (AIVO Orbit), we apply Cause 2 analysis - evidence invisible rather than evidence missing - to four major enterprise brands across distinct industry verticals: The North Face (consumer retail, VF Corporation), DocuSign (B2B SaaS), Akamai Technologies (cloud infrastructure), and Expedia (online travel). This revised version introduces a critical refinement to the Cause 2 diagnostic. Not all evidence invisibility is the same. Some gaps are deficiencies - closeable at low commercial cost, with no architectural rationale for their existence. Other gaps are trade-offs - the predictable consequence of architectural decisions that are commercially rational and would be expensive or destructive to reverse. A third category, propagation gaps, describes evidence that has been published correctly but has not yet propagated to the structured knowledge sources AI models rely on for entity definition. The three require fundamentally different remediation pathways. Treating them as equivalent produces recommendations that enterprise architecture leaders correctly reject as engineering naive. We document the strategic context in which each brand's infrastructure was designed, classify the resulting evidence gap as a deficiency, a trade-off, or a propagation lag, and prescribe remediation appropriate to that classification. Across the four brands the underlying observation holds: evidence the brand has produced is not reaching the AI model at the decision stage. The remediation, however, is brand-specific and infrastructure-specific. A single prescription applied uniformly produces both wasted engineering investment in cases where the architecture is sound and missed remediation opportunities in cases where the gap is genuinely a defect.
Sheals et al. (Mon,) studied this question.
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