This white paper defines AI Visibility 2.0, a governance-grade framework for measuring and managing brand visibility in generative AI assistants such as ChatGPT, Gemini, Claude, and Perplexity.
While early market offerings (AI Visibility 1.0) have relied on opaque dashboards and unverifiable claims of anonymization, AI Visibility 2.0 establishes a standards-based approach built on five pillars: transparency, reproducibility, attestation, governance alignment, and board-readiness.
Drawing on regulatory precedents (GDPR Recital 26, CPRA, FTC v. Kochava, and the draft EU AI Act), the paper critiques the structural deficiencies of first-generation solutions and sets out methodological requirements for visibility metrics that can withstand audit and regulatory scrutiny. It introduces the AIVO Standard v3.0 and the AIVO 100™ index as proof-of-concept implementations, while emphasizing that AI Visibility 2.0 is an open framework applicable to multiple methodologies.
The paper concludes with practical appendices, including an anonymization attestation checklist, a reproducibility testing protocol, and a model procurement clause for enterprises.
Intended audience: boards of directors, compliance officers, regulators, CMOs, agencies, investors, and analysts seeking to ensure that AI visibility reporting is legally defensible and governance-ready.
businessmate•1h ago
While early market offerings (AI Visibility 1.0) have relied on opaque dashboards and unverifiable claims of anonymization, AI Visibility 2.0 establishes a standards-based approach built on five pillars: transparency, reproducibility, attestation, governance alignment, and board-readiness.
Drawing on regulatory precedents (GDPR Recital 26, CPRA, FTC v. Kochava, and the draft EU AI Act), the paper critiques the structural deficiencies of first-generation solutions and sets out methodological requirements for visibility metrics that can withstand audit and regulatory scrutiny. It introduces the AIVO Standard v3.0 and the AIVO 100™ index as proof-of-concept implementations, while emphasizing that AI Visibility 2.0 is an open framework applicable to multiple methodologies.
The paper concludes with practical appendices, including an anonymization attestation checklist, a reproducibility testing protocol, and a model procurement clause for enterprises.
Intended audience: boards of directors, compliance officers, regulators, CMOs, agencies, investors, and analysts seeking to ensure that AI visibility reporting is legally defensible and governance-ready.