The Problem
Hospitals are inconsistently and inaccurately represented across major AI platforms (ChatGPT, Perplexity, Google AI Overviews), creating measurable patient diversion and brand trust risk.
The Opportunity
This exposure is measurable, repeatable, and scalable—positioning AI visibility as an emerging governance issue that warrants structured remediation.
The Framework
Kodiak's AI Risk Governance Framework provides ongoing monitoring and structured remediation across broader health systems.
Systemic Pattern Identified
Both hospitals show identical AI Readiness scores (45/100), indicating this is an industry-wide vulnerability, not an isolated issue. While overall readiness scores match numerically, the underlying contributing factors differ—TGH faces technical infrastructure gaps (missing schema markup), while NLH lacks foundational crawlability files (robots.txt, sitemap.xml). This indicates systemic exposure across distinct structural vulnerabilities.
Methodology Note: AI Readiness scores reflect weighted composite scoring across five standardized risk categories: technical infrastructure, entity consistency, competitive positioning, service-line authority, and monitoring capability.
Measurable Risk Categories
Competitors appear 60-70% more frequently in AI responses, directly diverting patient acquisition.
High-revenue specialties (cardiology, oncology) are underrepresented, creating measurable revenue risk.
Fragmented brand signals lead to patient confusion and trust erosion across AI platforms.
Zero visibility into AI representation creates ongoing, unmanaged governance exposure.
Repeatable Methodology
Query major AI platforms with standardized healthcare search patterns
Apply consistent scoring framework across 5 risk categories
Compare against competitors and industry standards
Generate standardized governance reports with remediation roadmap
This methodology uses publicly available data only and requires no internal system access, making it scalable across unlimited health systems with minimal friction.
Positioning AI Visibility as a Governance Issue
Why This is Governance, Not Marketing
- •Revenue Risk: Estimated multi-million dollar annual exposure per hospital based on competitive diversion modeling
- •Brand Trust: 20-30% inconsistency in AI-generated brand narratives
- •Competitive Exposure: Systematic disadvantage vs. competitors in AI discovery
- •Monitoring Gap: Zero visibility into emerging patient acquisition channel
Structured Remediation Required
Fix critical technical gaps, establish baseline AI presence
Build service-line authority, align entity signals
Continuous monitoring, quarterly governance reporting
What This Framework Is NOT
Not SEO or Marketing
This framework does not replace SEO, content marketing, or digital advertising efforts. It addresses AI-driven discovery risk within governance structures.
Not a Technical Fix
While technical remediation is required, this is fundamentally a governance and strategic positioning issue requiring C-suite oversight.
Not a One-Time Project
AI representation requires continuous monitoring and adaptation as platforms evolve. This is an ongoing governance function.
Not Hypothetical
The risks identified are based on actual AI platform responses and measurable competitive gaps, not speculative future scenarios.
Proof-of-Concept Scope & Limitations
This analysis is designed as a proof-of-concept to demonstrate the framework's viability and identify initial risk patterns. It is not a comprehensive diagnostic.
Recommendation: This proof-of-concept provides sufficient evidence to justify a Deep Diagnostic engagement, which would deliver comprehensive risk quantification and a structured remediation roadmap.
Evidence Snapshots: Real Query Examples
Concrete examples from actual AI platform queries demonstrate the measurable gaps in representation.
Example Query: "Best heart hospital Tampa"
Example Query: "Best cancer treatment Maine"
NLH: Missing Foundational Files
Methodology: This analysis was conducted using publicly available data across ChatGPT, Perplexity, and Google AI Overviews. No internal system access was required, making this framework immediately scalable across unlimited health systems.