How to rank in LLM search results

Healthcare professionals (HCPs) and patients are part of this shift too. HCPs are increasingly using AI systems to accelerate research, summarise clinical information, compare treatment options, and sense-check decisions. Patients are using them to interpret symptoms, understand diagnoses, and prepare for consultations.

This is more than an SEO trend. It is a strategic shift in discoverability, trust, and digital experience.

And for pharmaceutical companies, which historically held a near-monopoly on information about their own products, it raises a much bigger question than rankings alone: if AI increasingly becomes the interface between organisations and healthcare professionals, how do pharma companies remain visible, trusted, and genuinely useful within that relationship?

The disruption here is not purely technical. It is behavioural.

For years, internet users adapted themselves to search engines, simplifying questions into keywords and search syntax to retrieve useful results. AI reverses that relationship. Increasingly, technology adapts itself to human language instead, turning discovery from a navigational process into a conversational one.

Historically, search engines ranked pages, humans evaluated credibility, and websites competed for clicks. Now, AI systems increasingly sit between organisations and audiences, interpreting questions, synthesising information, and presenting answers before users ever interact with the source itself.

In healthcare and pharma, that matters enormously. AI is already shaping healthcare professional research, patient understanding, pre-consultation behaviour, and treatment exploration. In many cases, the first interaction with healthcare information may happen entirely inside an AI experience, often crossing regulatory boundaries in the process.

The question is no longer simply “How do we rank?” It becomes “How do we become a trusted source AI systems retrieve and cite?”

That distinction matters because visibility increasingly happens before the click. Influence shifts toward organisations whose information is structured, trusted, and useful enough to surface confidently within AI-generated responses. At the same time, the opportunity for engagement is concentrating around a much smaller group of trusted destinations. If AI systems can answer basic informational queries directly, then something more compelling is required for users to click through at all.

This is not merely a search change. It is a trust and discoverability shift. The organisations shaping AI answers will increasingly shape perception itself.

Across healthcare and pharma, concern is growing around the source of information being surfaced in AI systems, the regulatory implications of AI-generated healthcare answers, and the notable absence of many trusted pharma brands from cited sources altogether. Some organisations are beginning to explore “AI SEO”, “LLM optimisation”, "AEO", and new approaches to discoverability, while others are still assessing what this shift might mean strategically.

But beneath the tactical conversations sits a deeper issue. Healthcare and pharma have historically under-indexed on many of the foundational components that support AI retrievability: domain authority, semantic structure, metadata, interoperable knowledge systems, and connected content ecosystems.

Ironically, some of the most evidence-rich organisations in the world are among the least retrievable, and therefore among the least visible within AI-generated responses.

At the same time, some of the least regulated sources on the internet, such as Reddit, YouTube, and community forums, are highly visible because they are structured around accessibility, discoverability, discussion, and engagement.

Visibility in AI systems depends heavily on inferred signals of authority and confidence.

AI systems infer authority through signals such as citations, consistency, recognised expertise, semantic structure, connected ecosystems, and useful information. This is why the challenge extends beyond simply generating more content or retrofitting pages with FAQs. Producing larger volumes of AI-generated material is unlikely to create meaningful authority if the underlying information systems lack structure, trust, or relevance.

As synthetic content explodes across the web, average information becomes increasingly commoditised. Volume matters less. Signal quality matters more. The organisations most likely to succeed may not be those producing the most content, but those producing the clearest, most useful, and most structurally coherent knowledge.

The challenge is not simply optimisation. It is organisational readiness, openness and informational authority.

While the mechanics behind AI retrieval systems are still evolving, a few patterns are already becoming clear. Visibility in AI-generated answers increasingly appears to depend on five things: connected ecosystems, clear structure, trust signals, usefulness, and distinctive expertise.

Connected ecosystems

Perhaps the biggest shift is this: this is becoming less of a content problem and more of an information architecture problem.

The organisations best positioned for AI discoverability are unlikely to rely on isolated articles or disconnected campaigns. Instead, they will build connected ecosystems of interoperable information, reusable content architecture, and semantically coherent digital experiences.

Not something your typical pharma company has historically found easy.

Clear structure

AI systems need to interpret relationships between ideas quickly and reliably. Semantic hierarchy, explicit topics, and focused information architecture all make content easier to retrieve and synthesise.

Content that is fragmented, inconsistent, or poorly connected becomes harder for systems to interpret confidently, regardless of how accurate it may be.

Trust signals

Authority in AI systems is not declared. It is inferred.

AI systems appear to infer trust through signals such as citations, recognised expertise, transparent authorship, semantic structure, consistency, and credible domains. This is why the challenge extends beyond simply generating more content or retrofitting pages with FAQs.

Utility over promotion

Historically, pharma has under-indexed on genuinely useful content. Much of the industry’s digital output remains heavily opinionated, brand-led, and promotional, rather than task-oriented or clinically valuable.

Yet AI systems increasingly appear to reward content that helps users answer practical questions, support decisions, solve problems, and reduce friction.

Distinctive expertise

AI systems are extremely good at reproducing summaries and generic explanations, but they struggle to replicate genuine practitioner insight, first-hand experience, and original thinking.

As synthetic content floods the web, distinctive expertise becomes more valuable. In a world saturated with generated content, lived expertise may become one of the few remaining forms of differentiation.

Ranking alone is not enough.

If AI systems increasingly answer questions directly, then discoverability itself risks becoming commoditised. Traffic may decline structurally, and content alone may stop being meaningful differentiation.

If content becomes the entry point, then experience becomes the differentiator. The opportunity shifts toward tools instead of articles, ecosystems instead of campaigns, utility instead of exposure, and interaction instead of impressions.

There may also be strategic opportunities to think differently about where and how information is hosted, particularly as organisations navigate differing regulatory environments across regions. The source of information and the location in which it is consumed may increasingly diverge, creating entirely new questions around governance, discoverability, and digital strategy.

But the broader implication is simpler. If organisations want meaningful ongoing communication channels with healthcare professionals, answers are only the beginning. The value offered must extend beyond information retrieval itself.

For pharma companies in particular, this may require a fundamental shift in thinking. Historically, owning information about a product often meant controlling access to it. But in AI-mediated environments, information becomes increasingly distributed, synthesised, and contextualised outside the organisation itself. Visibility alone is no longer enough. The challenge becomes creating experiences, tools, and ecosystems valuable enough that healthcare professionals still choose to engage directly.

There is a significant opportunity here, particularly because much of pharma is still moving cautiously. In this environment, scale of publishing alone is unlikely to create meaningful advantage. Clarity, trust, usefulness, and structural coherence are becoming far more valuable than content volume itself.

The mechanics will continue to evolve, but one thing already feels increasingly clear: as AI makes information easier to generate and retrieve, information itself becomes less differentiating. What becomes harder, and therefore more valuable, is creating trusted, genuinely useful experiences people choose to return to.