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1. The Evidence Is No Longer Theoretical Under controlled, repeatable prompt classes across major AI systems: * Institutional ordering diverges * Narrative framing shifts under identical queries * Final recommendation resolution varies by model * Displacement patterns remain stable within execution windows This is not anecdotal. It is observable and reproducible. The phenomenon exists.

Abstract Where Case Study 1 documented platform-dependent divergence in oncology, this case documents cross-platform convergence in cardiology. Under identical decision-stage prompts, multiple AI systems consistently positioned the same therapy as preferred. The relevance lies not in clinical deviation, but in authority formation and the visibility of its

Platform Divergence and Decision-Stage Instability in Tier 1 Banking AIVO Journal — Banking Case Series Abstract Controlled cross-model testing across Tier 1 banking institutions demonstrates that identical comparative prompts can yield materially different decision-stage recommendations across leading AI systems. Under fixed prompt sequences and repeated runs, we observe:

Abstract Conversational AI systems are increasingly used at the point of therapeutic choice in oncology. In structured decision-stage testing across multiple leading AI systems, identical treatment-selection questions produced materially different “preferred therapy” outcomes depending solely on platform. The divergence did not arise from guideline deviation or factual error.

The Governance Blind Spot in External AI Systems AIVO Journal – Governance Commentary Abstract AI governance has concentrated on execution risk: whether agents act lawfully, whether decisions are compliant, whether liability can be assigned. This focus leaves a structural blind spot. Before execution, there is representation. Large language models shape how

AI assistants are compressing vendor evaluation into decision workflows that many organisations do not monitor. Over the past two years, SaaS competition has quietly shifted layers. The dominant question used to be: Are we visible? The emerging question is: Are we eligible? Structured multi-model observation across procurement-like prompt

Context The previous analyses in this series established two related problems: external AI systems now generate decision-relevant representations without organisational visibility, and optimisation-first approaches contaminate the evidentiary record organisations later need to evaluate their own interventions. The governance implication follows directly: Observation must precede intervention. The next question