The Search Landscape Has Shifted Under Pharma's Feet

The way healthcare professionals and patients find drug information has fundamentally changed. CMI Media Group research shows that 58% of pharma site visitors now arrive having first consulted ChatGPT. Meanwhile, 60% of searches end without a single click, 52% of pharma-related keywords now trigger AI Overviews, and click-through rates on organic results have plummeted from 25.8% to 7.4%.

These are not marginal shifts. They represent a structural change in how drug information is discovered, filtered, and delivered to the people who prescribe, recommend, and use medicines. If your brand is not present in AI-generated responses, it is increasingly absent from the conversation entirely.

This is the challenge that Generative Engine Optimization (GEO) was built to address. Drawing on analysis across 50+ PharmaGeo reports spanning obesity, IBD, allergy, heart failure, ophthalmology, and cardiomyopathy, covering OpenAI, Claude, Gemini, and Perplexity in English, French, German, and Japanese markets, the Aikka team has identified eight findings that define how pharma brands win (or lose) AI visibility in 2025.

What Is GEO?

Generative Engine Optimization is the practice of optimizing content and digital assets so they are cited, referenced, and surfaced by large language models (LLMs) in their responses. It was formalized by Aggarwal et al. at Princeton (KDD 2024), whose research demonstrated that targeted optimization strategies can increase a source's visibility in AI-generated responses by up to 40%.

GEO differs from traditional SEO in a fundamental way: instead of optimizing for a search engine algorithm that ranks links, you are optimizing for an AI system that synthesizes information and attributes claims to sources. The goal is not to rank first on a results page. It is to be the source the AI trusts, cites, and quotes.

For pharma, this distinction matters enormously. The sources an LLM trusts, and the hierarchy it applies to them, are not arbitrary. They are structured, measurable, and, to a significant extent, actionable.

Finding 01: AI Models Trust Regulators, Not Brands

The most important insight in pharma GEO is also the most humbling for brand teams: manufacturer websites are largely absent from the sources AI models cite.

Analysis of OpenAI citation behavior in the obesity category reveals that accessdata.fda.gov accounts for 6 of the top 10 most-cited documents. The Wegovy prescribing information alone was cited 54 times in a single month. NEJM publications of the SURMOUNT-1 and STEP 1 clinical trials follow at 22 to 24 citations each. Brand.com does not appear in the top tier.

This pattern reflects a clear, six-level source hierarchy that LLMs consistently apply to pharma content:

  1. Regulatory documents (FDA prescribing information, EMA SmPC, PMDA)
  2. Clinical guidelines (NICE, ACC/AHA, ESC)
  3. Peer-reviewed journals (NEJM, Lancet, PubMed Central)
  4. Medical references (UpToDate, Medscape)
  5. Health information sites (Mayo Clinic, NHS, WebMD)
  6. Manufacturer websites (brand.com)

This hierarchy is not a matter of opinion. It is empirically consistent with the Stanford SourceCheckup study published in Nature Communications (April 2025), which independently identified NIH, Mayo Clinic, and CDC as among the top-cited domains across LLM responses.

The strategic implication is direct: pharma brands that compete on the strength of their own websites alone are fighting on the wrong terrain. Visibility in AI responses requires authoritative presence in the sources AI already trusts, regulatory documents, clinical guidelines, peer-reviewed publications, and established medical reference platforms.

Finding 02: GEO Is Hyper-Local, Geography Reshapes Everything

A drug that dominates AI responses in US English queries may barely register in French or German. This is not a translation problem. It is a structural feature of how LLMs are trained and how they anchor to local clinical norms.

Cross-market analysis spanning the US, UK, Germany, France, and Japan reveals the depth of this divergence:

  • Entresto dominates US heart failure AI recommendations while SGLT2 inhibitors lead in Germany.
  • French-language IBD queries surface different biologic treatment options than the same queries in English.
  • LLMs anchor recommendations to local guidelines: NICE for UK queries, ACC/AHA for US, ESC for EU.
  • Japanese-language queries produce substantially different treatment hierarchies, reflecting PMDA approvals and J-guidelines rather than FDA or EMA documentation.
  • Regulatory lag is compounded in AI responses: drugs approved later in the EU continue to appear less frequently in AI responses even after their European launch, because the volume of indexed regulatory and clinical documentation has not yet caught up.

As Viseven's analysis of GEO for pharma notes, AI-driven health search is inherently local. A single global GEO strategy does not exist. Each market requires its own analysis, source mapping, and optimization plan calibrated to the regulatory authorities and clinical bodies that LLMs treat as credible in that geography.

Finding 03: AI Visibility Is Volatile, Monthly Shifts Are Significant

Pharma GEO is not a one-time optimization exercise. PharmaGeo Index data from the obesity category shows that AI brand visibility can shift materially from one month to the next, with changes large enough to alter competitive positioning.

Between March and April 2026, Saxenda's Appearance Rate dropped 10.2 percentage points. Mounjaro's Share of Voice more than tripled. Alli gained 6 percentage points in Appearance Rate in a single month.

The full picture across the obesity category:

Brand Mar Appearance Rate Apr Appearance Rate Change (pp) Mar Share of Voice Apr Share of Voice Change (pp)
Wegovy 91.3% 89.7% -1.6 19.3% 19.4% +0.1
Zepbound 81.5% 82.1% +0.6 17.2% 17.7% +0.5
Saxenda 57.6% 47.4% -10.2 12.2% 10.2% -2.0
Mounjaro ~7.6% 11.5% +3.9 0.7% 2.5% +1.8
Alli 26.1% 32.1% +6.0 5.5% 6.9% +1.4

Source: PharmaGeo Index

These swings are driven by changes in LLM training data, model updates, shifts in which sources are newly indexed or cited, and the publication of new clinical evidence. Unlike traditional SEO rankings, which tend to shift gradually, AI visibility can change significantly with a model update or the publication of a major guideline.

The lesson is straightforward: pharma brands need continuous monitoring infrastructure, not quarterly audits. A brand that was well-represented in AI responses in January may have a materially different profile by April, and without tracking, they will not know until patient and HCP behavior reveals the gap.

Finding 04: LLM Citations Are Unreliable, Creating a Pharma-Specific Risk

Not all visibility is beneficial visibility. The Stanford SourceCheckup study (Nature Communications, 2025) found that 50% to 90% of LLM responses are not fully supported by the sources they cite. Even GPT-4o with real-time web search fails to fully support nearly half of its own responses.

For pharma, this creates a category of risk that has no equivalent in traditional SEO. An AI model may cite a drug's FDA prescribing information while simultaneously misrepresenting the approved dosing range, understating a contraindication, or presenting off-label uses without appropriate qualification. The citation lends the response an appearance of authority that the underlying content does not warrant.

The consequences extend across three domains:

  • Pharmacovigilance blind spots: Patients or caregivers acting on AI-generated drug information that incorrectly represents safety profiles, without anyone in the system having flagged the misrepresentation.
  • Regulatory exposure: AI-generated content that associates a brand with unapproved indications or inaccurate safety claims, even when citing legitimate regulatory documents.
  • HCP misinformation: Prescribers using AI as a rapid reference who receive subtly inaccurate summaries of complex clinical data.

As performance.io's analysis of AI search in pharma notes, the industry's challenge is not only to be cited more often, but to ensure that what AI says when it cites a brand is accurate and complete. This requires active monitoring of AI response quality, not just citation frequency.

Finding 05: Legacy Brands Have Structural GEO Advantages

One of the less intuitive findings from Aikka's research is that older drugs frequently outperform newer therapies in AI visibility. This is not because they are better treatments. It is because they have more accumulated documentation.

In the obesity category, Qsymia (approved 2012) and Contrave (approved 2014) both maintain 56% Appearance Rates in AI responses. Meanwhile, newer entrants with stronger clinical profiles and more recent approvals start at a structural disadvantage because they have fewer indexed documents feeding the citation networks AI relies on.

The mechanism is straightforward. A drug approved in 2012 has accumulated over a decade of FDA regulatory updates, guideline inclusions, Wikipedia edits, clinical review articles, and PubMed citations. Each of these documents links to others, forming a dense citation network that AI models navigate when constructing responses. A drug approved in 2023 starts with a much thinner network, even if its clinical data is superior.

This finding, highlighted by Evertune's GEO framework for pharma, has direct implications for launch strategy. New market entrants cannot rely on the quality of their data alone. They need to actively accelerate the construction of their citation footprint from day one, prioritizing regulatory documentation accessibility, clinical publication indexing, and guideline inclusion.

Finding 06: Clinical Trials Are Your Most Valuable GEO Asset

If there is one single action that has the greatest impact on pharma GEO, it is ensuring that pivotal trial publications are fully indexed on PubMed Central with open full-text access.

The data is consistent across therapeutic areas:

  • SURMOUNT-1 and STEP 1, both published in NEJM, are cited dozens of times monthly for obesity queries.
  • GEMINI and VARSITY trial data drove Entyvio's visibility in IBD AI responses.
  • DAPA-HF and EMPEROR trials were the primary determinants of heart failure treatment recommendations in AI responses.

The pattern is direct: AI models treat NEJM and Lancet trial publications as authoritative clinical sources, and they cite them repeatedly when answering questions about treatment options. If your pivotal trial is not indexed on PubMed Central with full-text access, your drug is functionally invisible to AI, regardless of its clinical merits.

This creates a clear priority for medical affairs and publication planning teams. Full-text PubMed Central indexing is not optional. It is infrastructure. The same applies to ensuring that trial results are accurately reflected in existing guideline documents and that the trial's Wikipedia entry (where one exists) is current and properly cited.

Finding 07: The Wikipedia Effect Is Real and Measurable

Wikipedia is not a source that most pharma brand teams actively manage. It should be.

AI models cite Wikipedia articles on drug classes, specifically articles like "GLP-1 receptor agonists" and "biologic therapy for IBD," more frequently than any pharma-owned content. These drug class articles function as AI's first reference point when constructing comparative responses, and the brands mentioned, the clinical data cited, and the framing used in those articles directly shape what the AI then says about individual products.

The measurable finding: brands with well-maintained Wikipedia pages that include accurate FDA and EMA citations, up-to-date pivotal trial results, and proper sourcing see better AI visibility than comparable brands with outdated or incomplete Wikipedia entries.

Wikipedia optimization for pharma requires adherence to the platform's editorial policies, which prohibit promotional content and require neutral point of view sourcing. The appropriate approach is to ensure that published facts, regulatory approvals, and trial results are accurately reflected, properly sourced to peer-reviewed publications and regulatory documents, and kept current. This is not brand promotion. It is factual documentation that AI systems then treat as a credible reference.

Finding 08: Schema Markup Is an Unfair Advantage Most Pharma Brands Ignore

Structured data using Schema.org vocabulary, specifically the Drug, MedicalCondition, and FAQPage schema types, provides AI systems with machine-readable signals about the content and credibility of a web page.

Microsoft has explicitly confirmed that schema markup helps AI systems cite content correctly. Brands that have implemented health-specific structured data appear more consistently in AI responses than competitors with equivalent content quality but no schema implementation.

The market opportunity here is significant because the adoption gap is large. Most pharma companies have not implemented health-specific schema markup on their brand or medical information websites. For brands willing to invest in the technical implementation, this is a first-mover advantage with measurable impact on AI citation rates.

The specific schema types most relevant to pharma include:

  • Drug schema for prescribing information pages, with properties for dosing, indications, and contraindications
  • MedicalCondition schema for disease education content
  • FAQPage schema for patient and HCP FAQ sections
  • MedicalStudy schema for clinical trial results pages

Implementation is a one-time technical investment with compounding returns as AI models continue to grow their share of health information queries.

The 6-Pillar GEO Framework for Pharma

These eight findings point toward a coherent framework for pharma GEO strategy. Based on Aikka's analysis across therapeutic areas and markets, effective pharma GEO requires attention to six interdependent pillars:

1. Regulatory Source Optimization Ensure that all regulatory documents, prescribing information, SmPCs, and REMS materials are accessible, properly indexed, and accurately represent current approved labeling. Regulatory documents are Tier 1 sources in the AI hierarchy. They must be treated as GEO assets.

2. Clinical Evidence Amplification Prioritize full-text PubMed Central indexing for all pivotal trial publications. Ensure trial results are accurately reflected in guideline documents and Wikipedia entries. Treat the clinical publication record as infrastructure, not just science communication.

3. Market-Specific GEO Build separate GEO programs for each major market, aligned to local regulatory authorities (FDA, EMA, PMDA, NICE) and local clinical guidelines (ACC/AHA, ESC, J-guidelines). AI visibility is local by default.

4. Structured Data Implementation Deploy Schema.org health markup (Drug, MedicalCondition, FAQPage) across all owned digital properties. This is a technical first-mover opportunity most competitors have not yet captured.

5. Multi-Source Distribution Extend content presence to the full citation hierarchy: clinical guidelines, peer-reviewed publications, established medical reference platforms, and health information sites. Brand-owned channels are Tier 6. Tier 1 through Tier 5 require active, coordinated effort.

6. Continuous AI Visibility Monitoring Implement monthly tracking of Appearance Rate, Share of Voice, and citation accuracy across all target LLMs, markets, and queries. AI visibility is volatile. Quarterly audits are insufficient. Build monitoring infrastructure that can detect shifts in real time.

Methodology

The findings presented in this article are based on analysis of 50+ PharmaGeo reports produced by the Aikka team across six therapeutic areas: obesity, IBD, allergy, heart failure, ophthalmology, and cardiomyopathy. Data collection covered four major LLM platforms, including OpenAI, Claude, Gemini, and Perplexity, across English, French, German, and Japanese market queries. Longitudinal brand visibility tracking draws on the PharmaGeo Index, which monitors Appearance Rate and Share of Voice metrics monthly across a defined query set.

Conclusion: AI Visibility Is Now a Competitive Moat

The pharma brands that will lead in AI-mediated discovery are not necessarily the ones with the largest marketing budgets or the newest therapies. They are the ones that understand how AI systems evaluate and cite drug information, and that build their digital presence accordingly.

The six-pillar framework above is not a theoretical model. It is derived from what measurably works, across multiple LLMs, multiple markets, and multiple therapeutic categories. The data is clear: regulatory source quality, clinical trial indexing, Wikipedia presence, schema markup, market-specific strategy, and continuous monitoring are the determinants of pharma GEO performance.

The window for first-mover advantage is open but not permanently. As more pharma teams build GEO capability, the structural advantages available today will narrow. The brands that act now will build citation networks and structured data assets that compound over time.

To explore how your brand performs in AI responses today, visit the PharmaGeo Index.

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Data source: PharmaGEO platform analysis of 50+ reports across OpenAI, Claude, Gemini, and Perplexity (2025-2026)