From SEO to GEO: Retooling for the Zero-Click Content Era

Pharma’s digital engagement model is undergoing a structural shift. Search is now conversational, answers are summarized instantly, and 60% of Google search queries end without a single click to a brand’s owned asset. In this emerging zero-click content landscape, traditional pharma SEO strategies, and CTR-based measurement models are being quietly made obsolete.

This blog draws on insights from a keynote by Tarun Mathur, Chief Technology Officer at Indegene, delivered at the Indegene Digital Summit 2025, where the discussion revealed how AI-driven discovery is redefining how pharma content is created, governed, and measured.

The key message for pharma leaders is clear:  AI agents are becoming the primary consumers and disseminators of information. Pharma organizations that adapt their strategies for these agentic AI ecosystems will own the next era of trusted engagement.

The industry has been operating under a dated assumption: publish high-quality, compliant content, drive traffic to owned sources, and measure success through clicks and conversions. That model is collapsing. We are entering the zero-click content era, where users ask full, contextual questions and receive synthesized answers directly in AI overviews, assistants, or copilots without ever landing on a brand site.

For pharma SEO, this means that a growing share of HCP and patient questions are now resolved before reaching owned assets. Research shows that at least 63% of healthcare-related searches already trigger an AI-generated summary, especially informational queries such as those around disease education, safety, dosing, or access.

Reach is no longer defined by how many people enter your site; it’s defined by how accurately and consistently your narrative appears inside AI-generated answers. Pharma marketers need to start treating “presence in AI answers” as the new share of voice

Audiences increasingly access information through AI assistants and search copilots that interpret and summarize multiple data sources before presenting them to users. These systems act as intermediaries between pharma content and human audiences, influencing how messages are represented and trusted.

This shift creates a new engagement mandate for pharma SEO teams. We’re no longer only communicating with a physician or a patient. We are communicating with the AI agent advising them. That agent is making judgment calls on trustworthiness, safety, and accuracy in real time.

This is especially sensitive in pharma. The risk of hallucinated guidance on dosing, safety, or support programs is not theoretical. It’s regulatory, reputational, and clinical. Building AI-readable, verifiable, and compliant zero-click content becomes essential to maintaining credibility in this new environment.

For years, pharma SEO strategies determined how content is structured: high-value keywords, crawlable pages, well-governed metadata, backlinks. That remains useful but it’s no longer sufficient. The next evolution is Generative Engine Optimization (GEO). GEO is the discipline of structuring your content so that AI agents can both understand it and trust it.

The content patterns that help an AI agent generate contextually accurate answers are not the same patterns that help Google rank a page. GEO transforms traditional SEO into a discipline optimized for zero-click content visibility. It requires content to:

Why this matters: Large language models (LLMs) such as those powering AI Overviews or copilots are fundamentally trained on Q&A pairs with reasoning traces. Content that mirrors this structure is more likely to be ingested, referenced, and surfaced. Optimizing for generative engines can improve inclusion in AI-generated summaries by as much as 40%.

In short, GEO represents the next stage of pharma SEO, designed for discoverability in the zero-click content ecosystem and purpose-built for earning representation inside AI outputs.

In the old model, an AI assistant scrapes your site, pulls unstructured text into its own short-term memory, and generates an answer locally. That creates variability, weakens traceability, and increases the chance of hallucination.

In the emerging model, the site no longer waits passively to be scraped. Instead, it exposes an agent-facing interface using standards like Model Context Protocol (MCP), A2A (agent-to-agent), or NL Web. That allows external AI agents to interact with source of truth and retrieve an approved, citation-backed answer.

Instead of third-party AI systems paraphrasing the content, they are effectively querying it. Operationally, this means pharma organizations can embed medical accuracy guardrails, improve auditability, and enhance AI trust scores. AI systems are more likely to surface content that’s authoritative, verifiable, and machine-queryable, turning compliance into a competitive advantage.

If AI is now the dominant distribution mechanism, then AI observability becomes the new analytics layer. Instead of measuring clicks, pharma SEO teams can monitor how frequently your zero-click content narratives appear in AI-generated responses.

Emerging observability tools now allow pharma SEO teams to:

This transition from click metrics to AI presence marks the most profound shift in how engagement quality will be defined across pharma.

A practical concern for pharma leaders is scalability. Converting hundreds of pages of HCP, patient, and access content into fluent, compliant, Q&A-style machine-ready datasets sounds like a multi-year reauthoring project. Automated multi-agent systems can now accelerate this transformation.

Task agents: Handle retrieval and formatting, scanning websites or content libraries and converting text into structured, question-and-answer datasets.

SME (subject matter expert) agents: Bring in the domain context, understanding the intent of patients, HCPs, and regulators. These are the agents that task agents can consult with while retrieving information.

These SME agents are essential. They interpret the nuances of medical terminology, audience sensitivity, therapeutic relevance and real-world intent.

Together, this agentic framework can produce high-fidelity, compliant datasets suitable for both AI interfaces. By orchestrating collaboration between task and SME agents, pharma organizations can scale zero-click content transformation across brands without compromising compliance or accuracy.

The conversation at Indegene Digital Summit 2025 made one thing unmistakably clear: AI transformation in pharma content is not about making more content, faster. It’s about redesigning how content flows. We are moving from a model where we publish to humans and hope they find us, to a model where we publish to agents and expect them to represent our truths. The future of engagement will depend on how effectively organizations design, govern, and measure their content for both human and machine interpretation.