We Analyzed AI Visibility Across 25 Pharmaceutical Manufacturers. Here’s What We Found.

Pharmaceutical brands operate in one of the most evidence-sensitive categories in AI search. People ask AI systems about treatment options, eligibility, side effects, dosing, biomarkers, disease management, and the companies developing new therapies. These are not casual questions. They can shape what patients discuss with clinicians, what healthcare professionals investigate next, and which sources become part of the decision journey.

We ran GEO reports across 25 pharmaceutical manufacturers selected from the Fierce Pharma Week 2025 attendee list. Each report tested 20 neutral, discovery-oriented prompts across leading AI providers, producing 160 searches per company and 4,000 searches in aggregate.

The pattern was consistent: AI systems often recognized a medicine, therapy area, or manufacturer without treating the manufacturer’s corporate domain as the source. The average brand mention rate was 7.8%. The average first-party citation rate was 3.2%. The median company was cited in only 2% of searches.

For pharma marketers, the issue is bigger than awareness. It is evidence ownership. If an AI answer names the therapy but cites a regulator, journal, hospital, guideline body, advocacy organization, partner, or publisher, the brand is present while the first-party source is absent.

Across the 25 manufacturers, the average brand mention rate was 7.8% and the average corporate-domain citation rate was 3.2%. The average gap was 4.6 percentage points.

That may sound smaller than the gaps in consumer categories, but the baseline is far lower. The median company appeared in 6% of searches and earned citations in just 2%. Across all 4,000 searches, the reports recorded 315 brand mentions and only 127 first-party citations.

The distribution was also unusually concentrated. Amneal Pharmaceuticals posted a 41% mention rate and a 24% citation rate, four times the citation rate of the next-highest companies. Remove that outlier and the industry picture becomes even flatter. Most manufacturers in the sample earned first-party citations in 0% to 4% of searches.

The through-line: a large portfolio, a familiar corporate name, and strong product awareness do not automatically create first-party authority in AI answers.

Four companies in the study received no citations to the analyzed corporate domain: Sanofi, Endo, Exelixis, and Zambon Group.

The reasons were not identical.

Sanofi was mentioned in 6% of searches, especially around atopic dermatitis, infant RSV prevention, hemophilia, and multiple sclerosis. Yet none of 158 successful responses cited sanofi.com. AI systems recognized parts of the portfolio but sourced clinical authorities and competing manufacturers instead.

Exelixis showed a similar medicine-to-company disconnect. Its flagship therapy appeared in answers for kidney, liver, neuroendocrine, and thyroid cancers, but exelixis.com received no citations across 160 successful responses. Product relevance existed. Corporate-domain authority did not.

Endo’s transition across Endo, Keenova Therapeutics, and Par Health introduced a different problem: fragmented entity signals. The company was mentioned around ready-to-use hospital medicines and domestic generic manufacturing, but no answer cited the analyzed domain.

Zambon Group had the deepest visibility gap. It received neither a mention nor a citation across 150 successful responses covering Parkinson’s care, respiratory care, rare disease, and pharmaceutical manufacturing services.

A 0% citation rate is not one diagnosis. It can point to missing clinical evidence pages, weak product-to-company attribution, fragmented corporate entities, limited third-party authority, or content that does not directly answer neutral treatment questions.

Across nearly every therapeutic area, AI systems preferred sources built to validate clinical claims.

The recurring winners included regulators, government health agencies, professional guidelines, peer-reviewed journals, major hospitals, medical publishers, and patient organizations. These sources tend to provide the elements AI answers need: explicit indications, eligibility criteria, comparative factors, safety context, study references, visible review dates, and stable pages.

This creates a distinctive pharma citation pattern. In consumer categories, brands often lose citations to aggregators or review sites. In pharmaceuticals, manufacturers lose the evidence layer to clinical authorities.

That does not mean first-party content should imitate a guideline or replace independent medical information. It means manufacturer pages need to make approved evidence easier to retrieve and verify. A broad therapeutic-area page with portfolio language is less useful to an AI system than a focused page that clearly explains who a treatment is for, what evidence supports it, where the approved-use boundaries sit, when the page was reviewed, and which primary sources substantiate each claim.

The strongest recurring recommendation across the reports was to turn scattered product, newsroom, medical-affairs, trial, and corporate material into durable, crawlable evidence hubs.

Several companies had meaningful medicine or pipeline recognition but little first-party citation authority.

Daiichi Sankyo appeared in 10% of responses, driven by antibody-drug conjugate leadership and product visibility, but its corporate site was cited in only 1%. Servier posted a 9% mention rate and a 1% citation rate. Astellas reached 8% mentions and 1% citations. Novo Nordisk appeared in 6% of answers but received citations in only 1%.

The same issue appeared at larger scales. Merck recorded a 9% mention rate and 3% citation rate. CSL Behring reached 11% mentions and 2% citations. Regeneron reached 9% mentions and 3% citations.

In many of these reports, AI systems named a product but cited the FDA, a journal, a medical publisher, a partner, a product-specific site, or another clinical source. The corporate domain did not consistently receive credit for the company’s own medicine-level relevance.

The operational fix is entity clarity. Corporate, product, disease-area, newsroom, investor, and medical-affairs pages need consistent ownership language, stable canonical URLs, reciprocal links, structured organization and product information, and a clear path from each medicine to its supporting first-party evidence.

Brand awareness is not the same as source authority. Pharma marketers need to measure both.

The strongest visibility did not come from the broadest corporate claims. It came from specific questions where a company had a clear evidence advantage.

Amneal’s 24% citation rate was powered by focused strengths: complex generics, ready-to-use oncology injectables, and cross-category specialty positioning. Its pages gave AI systems more concrete reasons to cite the company than a generic corporate overview would.

Boehringer Ingelheim earned its strongest visibility from poultry disease-prevention content. Lundbeck broke through on brain-health and cross-neuroscience expertise. Bristol Myers Squibb performed best on specific melanoma, biomarker-driven colorectal cancer, large B-cell lymphoma, and lower-risk myelodysplastic syndrome questions. Novartis was strongest in radioligand therapy and selected kidney and immunology topics. Regeneron’s clearest wins came from rare diseases, especially CHAPLE disease and inherited hearing loss.

The pattern also held for smaller manufacturers. Azurity’s strongest results came from liquid formulations tied to specific patient and treatment needs. Ferring earned visibility around microbiome-based recurrent C. difficile treatment and personalized IVF dosing. Jazz Pharmaceuticals broke through on high-intent sleep and blood cancer questions.

AI systems cite definitive answers to narrow questions. Broad claims about innovation, leadership, or portfolio breadth rarely provide enough evidence to earn the link.

Several companies earned isolated citations through press releases, investor updates, or newsroom pages. Those pages can be specific, timely, and rich in approval or trial details, which makes them attractive sources for AI answers.

But a citation to a time-bound announcement is fragile. The answer may depend on a dated headline, an investor-oriented page, or a regional release rather than a stable clinical explainer on the main domain.

The reports repeatedly recommended converting announcement-level evidence into evergreen assets. That means maintaining durable pages for indications, treatment pathways, trial outcomes, eligibility, safety context, regulatory milestones, and frequently asked questions. Each related release should link to the canonical evidence page, and the canonical page should carry a visible update history.

This is especially important in pharma because evidence changes. A durable page can be reviewed, updated, governed, and connected to the latest approved sources. A press release can announce the moment. It should not be the only page that explains it.

For many manufacturers, the first opportunity is not to become more famous. It is to convert existing recognition into source authority.

Amneal had a 17-point gap between mentions and citations. Azurity’s gap was 10 points. CSL Behring’s was 9 points. Daiichi Sankyo and Servier each had 9-point gaps. Astellas had a 7-point gap. Merck had a 6-point gap.

These companies already appear in relevant answers. The next step is to give AI systems a better first-party page to cite.

The most common report recommendations were remarkably consistent:

1. Create neutral, question-led treatment and disease hubs. Organize content around eligibility, treatment selection, biomarkers, administration, monitoring, safety, and patient or clinician decision factors.
2. Connect products to the corporate source. Use consistent manufacturer attribution, canonical URLs, internal links, and structured organization and product information across corporate and product properties.
3. Make evidence extractable. Add concise answer blocks, comparison tables, trial summaries, visible review dates, named medical reviewers, and direct references to primary evidence.
4. Turn announcements into evergreen evidence pages. Preserve the specificity of regulatory and trial news in maintained, canonical resources.
5. Earn third-party authority. Accurate inclusion in guidelines, medical society education, peer-reviewed reviews, advocacy resources, and trusted clinical publications reinforces the evidence network AI systems already use.
6. Prioritize owned niches. Build depth where the company has distinctive evidence instead of trying to win every broad category query at once.

Pharma GEO is not a volume contest. It is a governed evidence operation.

The teams most likely to improve visibility will align medical, legal, regulatory, communications, web, SEO, analytics, and brand operations around a shared set of discovery questions. They will identify where products are already mentioned without a first-party citation, map each question to an approved source page, and close the gaps in content, attribution, structure, and authority.

That work requires more than writing new copy. It includes assembling approved evidence, routing review, maintaining medical accuracy, connecting content across corporate and product properties, validating structured information, monitoring AI answers, and updating pages when indications, guidelines, evidence, or market context change.

The upside is measurable. A company does not need to dominate every treatment conversation to improve its position. It needs to become the clearest, most current, and most verifiable first-party source for the specific questions it has the right to answer.

To see where your brand specifically stands, explore Gradial GEO execution.

Each company report used 20 neutral, discovery-oriented prompts across leading AI providers, producing 160 searches per company and 4,000 searches in total. The dataset contained 3,807 successful responses. Prompts covered company-specific therapeutic areas and user-like discovery intents, including treatment options, eligibility, comparisons, administration, evidence, and manufacturer expertise.

Headline mention and citation figures use each report’s top-line scorecard metrics as the authoritative values. Company-level rates were averaged without weighting by response count so each manufacturer contributed equally to the industry benchmarks. This study measures whether the analyzed corporate domain was cited. It does not measure the total visibility of every product site, regional domain, partner page, or third-party source associated with a manufacturer.

The findings describe AI search visibility, not clinical quality, treatment efficacy, safety, or medical suitability. Nothing in this analysis is medical advice.

This is part of Gradial’s ongoing research into AI search visibility across industries. To see how your brand shows up in AI-generated answers, explore Gradial GEO execution.