Pharma SEO Isn’t Enough Anymore.
In January 2026, journalists reported that Google’s AI Overviews had produced misleading summaries for some health-related queries, including incorrect interpretations of blood test ranges and medical advice that lacked essential clinical context. Google later removed some of these AI-generated answers after review, acknowledging they were potentially misleading for health searches[1].
What matters here is not that the source content was wrong. In several cases, it came from reputable healthcare websites. The issue was that nuance was lost when the content was summarised. Conditional information became absolute. Context disappeared. The AI answer looked confident and authoritative.
That is the situation pharma teams now need to design for.
Why Generative Engine Optimisation (GEO) matters now
Search is no longer just about sending people to websites. Increasingly, it is about providing answers directly.
Tools such as Google AI Overviews and Microsoft Copilot summarise content on the search results page. Often, users do not click through at all. This zero-click behaviour is becoming normal for informational queries.
At the same time, use of AI tools in healthcare is growing in real clinical environments, even if adoption varies by setting.
Separate UK studies published in late 2025 suggest that between 25 and 28 percent of general practitioners now use generative AI tools, with adoption increasing compared with previous year[2][3]. These figures show that AI tools are already influencing how healthcare professionals access and process information, and that usage is increasing rather than declining.
For healthcare professionals, this means content on clinical topics is increasingly shaped by AI-driven summaries and recommendations. AI answers are used as a quick first check. Summaries are read before guidelines or primary sources. Content influences understanding even when the website is never visited.
This means pharma content can shape interpretation without direct engagement. Being correct is no longer enough. Content must also survive being summarised.
SEO vs GEO: what stays the same and what changes
You may see Generative Engine Optimisation (GEO) referred to using different terms, including Answer Engine Optimisation (AEO), AI SEO, or AIO. While the names vary, they all describe the same underlying shift: content is no longer just ranked and clicked, it is increasingly summarised and reused by AI systems. In this article, GEO is used as a catch-all term for that change.
GEO does not replace SEO. Most of the fundamentals are the same and still necessary.
SEO focuses on helping pages be discovered and ranked.
GEO focuses on helping content be correctly interpreted when it is summarised, quoted, or reused by AI systems.
What stays the same:
- Clear site structure
- Logical headings
- Accurate and compliant content
- Authority and trust signals
- Ongoing content review and maintenance
What changes:
- Content is often read out of context
- Individual sentences matter more than whole pages
- AI systems prefer concrete, clearly scoped facts
- Ambiguity is more likely to be simplified or lost
A useful way to think about it is this:
SEO asks whether a page can be found.
GEO asks whether a sentence can be reused without changing its meaning.
Practical GEO actions you can take
1. Use clear headings to keep each section focused on one topic
Headings are one of the strongest signals AI systems use to understand what a section is about.
Good headings clearly state intent and help keep content tightly on topic. Each section should cover one concept only and avoid drifting between definition, benefit, and instruction.
Good headings include:
- What is Condition X
- Indication
- Patient population
- Limitations of use
- Educational overview
- Frequently asked questions
Poor headings include:
- Overview
- Background
- Details
If a human can scan the headings and understand the structure immediately, an AI system is far more likely to summarise it correctly.
2. Use bullets, tables, and FAQs to make content easy to extract
AI systems favour content that is already structured into clear, discrete units.
Use:
- Bullet points for lists of criteria, limitations, or key facts
- Tables for comparisons or conditions
- FAQ sections for direct questions and answers
Each list, table, or FAQ should stay tightly focused on a single topic. Avoid mixing explanations, instructions, and outcomes in the same block.
3. Write sentences that still make sense on their own
AI systems often lift individual sentences or short blocks of text. Anything that relies on surrounding context is fragile.
Risky:
This treatment is effective when used as recommended.
Clearer:
This treatment has demonstrated efficacy in adults with condition X when used in combination with Y, based on the approved prescribing information.
The clearer sentence carries scope, population, and authority with it.
4. Be specific with facts, numbers, and timeframes
AI systems strongly prefer concrete information. Vague wording is more likely to be simplified or misinterpreted.
Vague:
Studies show improved outcomes.
Specific:
In a Phase III study of 842 adults, treatment X reduced outcome Y by 28 percent compared with placebo.
Where possible:
- include key statistics or metrics
- include dates
- include comparators
Specificity improves both accuracy and reliability when content is summarised.
5. Use clear, visible references to support key claims
Wherever possible, show where important facts come from.
This can include:
- Superscript references such as [1] that link to a reference list at the end of the page
- Explicit mentions of sources in the text
- Direct references to approved documents such as the SmPC or published guidance
The aim is to make it obvious which statements are evidence based. This helps AI systems distinguish established facts from explanation or commentary and reduces the risk of unsupported claims being over summarised.
6. Use short definition sections for important terms
If a term matters, give it its own short section instead of burying the explanation in long prose.
Example:
What is Mechanism Y
Mechanism Y describes how Drug A inhibits pathway Z, reducing activity in process Q.
This works because the definition is self contained and clearly signposted. It can be quoted safely without pulling in unrelated information.
7. Make content freshness visible
AI systems often choose between multiple similar sources. Clear freshness signals help them make better choices.
Good examples:
- Last reviewed March 2026
- Information current as of Q1 2026
- Data relates to studies published between 2021 and 2024
Visible dates can help AI systems prioritise newer, more accurate content over older material that might be easier to extract.
Common pitfalls in AI-driven search
Some common writing habits increase the risk of misinterpretation:
- Do not hide caveats deep in paragraphs
- Do not rely on earlier sections to define important limitations
- Do not assume approved wording is safe when quoted on its own
- Do not mix mechanism, indication, and outcome in the same sentence
For example, avoid sentences such as:
Drug X inhibits pathway Y and improves survival in patients with condition Z.
This combines mechanism, outcome, and population in a way that is easy to misquote when summarised.
Approval ensures accuracy. It does not guarantee that content can be safely summarised.
A note on visibility and exclusion from search
Search engines and AI systems rely on access to web pages in order to quote or summarise them. It is possible to instruct platforms such as Google, Bing, Gemini, Copilot, ChatGPT, and Perplexity to exclude specific pages from search and AI-driven results.
This means:
- You can reduce the risk of content being quoted or summarised by AI systems by excluding it from search
- Doing so also prevents users from finding that content through both traditional search results and AI-generated answers
Excluding content can reduce the risk of misinterpretation, but it also reduces visibility and may shift influence to third-party sources instead.
This is a trade-off that teams should be aware of, not a recommendation.
What this means for pharma teams building content today
SEO taught us how to be found.
AI-driven search forces us to think about how content is understood when it is taken out of context.
For pharma, this matters more than in most sectors. AI systems will continue to compress information. The responsibility now includes making sure that compression does not change meaning.
Clear structure, explicit scope, and careful wording are no longer just good practice. They are part of building responsibly in an AI-mediated search world.
References
[1] The Guardian. Google AI overviews removed after misleading health information identified. January 2026.
[2] The Guardian. GPs increasingly turning to AI tools, study finds. December 3, 2025.
[3] Varn Health. How are HCPs searching and using AI? 2025.
Last updated 4th Feb 2026