When Patients Ask ChatGPT: How OTC Brands Can Stay Visible in the Age of AI Search

Patients are increasingly turning to conversational AI platforms like ChatGPT for health information, effectively making these tools the first point of discovery for everyday symptoms and treatment advice. In this new paradigm, asking an AI about a headache or a cough has begun to replace the old habit of Googling symptoms. Importantly for over-the-counter (OTC) health brands, large language models (LLMs) such as ChatGPT or Google’s AI-based search results now determine which treatments or brands get mentioned to consumers. This whitepaper explores the rise of AI-assisted health search, how LLMs interpret common OTC categories (with examples like reflux, cold & flu, and pain relief), why a brand’s visibility in AI-generated answers depends on structured, credible data, and a practical roadmap for auditing and engineering your brand’s presence in ChatGPT and Google’s AI search.

The Rise of AI-Assisted Health Search

The way consumers seek health information is undergoing a generational shift. ChatGPT has quickly become one of the top 10 most-visited websites globally, with users even setting it as their default search for answers. Emerging AI search engines (e.g. Perplexity, Bing Chat, Amazon’s “Ask Alexa” upgrades) now handle hundreds of millions of queries per week. According to a recent Andreessen Horowitz study, 60% of U.S. consumers used an AI chatbot to research or decide on a product in the last 30 days – a striking indication that conversational AI is entering everyday life. Health is a major part of this trend: industry analysis shows health is among the top categories on generative AI platforms, accounting for roughly 560 million monthly brand mentions in ChatGPT conversations. In other words, consumers are asking AI about medical conditions, remedies, and OTC products at massive scale.

Generative AI referral traffic by category. Health is one of the top categories for AI-driven brand mentions (an estimated 560 million monthly mentions in ChatGPT conversations), reflecting how often consumers ask AI about medical and wellness topics. Only news/media and entertainment see higher engagement. This trend underscores that healthcare brands cannot ignore AI as a major search channel.

Several factors underlie the rise of AI-assisted search. First, conversational convenience: people can query an AI agent in natural language and get a concise answer or advice. In fact, the average query on new AI engines is far longer and more specific than on Google – 10+ words on Perplexity vs. 2–3 keywords on Google, with about half of AI queries leading to follow-up questions. Consumers treat AI like a virtual advisor, asking detailed questions as they would a person. Second, there is a growing comfort with AI’s guidance in sensitive areas like health. In one survey, 25% of people said they would rather consult AI than a doctor for a minor health issue, and 80% of ChatGPT users reported it gave effective medical advice. While AI is not a replacement for professional care, these statistics highlight the trust and reliance being placed on AI for preliminary health information.

Finally, the search landscape itself is evolving. Google remains dominant, but it’s augmenting traditional search with generative AI summaries (the Search Generative Experience, or SGE). Instead of ten blue links, Google’s SGE can present an AI-generated overview of, say, “best treatments for seasonal allergies,” pulling from trusted web sources. Early analyses show that health and pharmaceutical content are among the most likely to appear in Google’s AI-driven answers – a sign that Google’s algorithms favor authoritative, structured sources for health queries. In July 2025, Google even launched an “AI Mode” in select markets, letting users toggle to a chatbot-style interface for search. All these developments point to one conclusion: AI-driven search is becoming mainstream for health information discovery, and brands must adapt to remain visible.

How LLMs Interpret OTC Health Questions (Reflux, Cold & Flu, Pain Relief)

When a patient asks an AI like ChatGPT about a common ailment, the response is very different from a traditional search engine results page. Rather than listing a dozen websites, the AI synthesizes a single answer drawn from its training data and real-time sources. This answer tends to be holistic and generic, focusing on symptoms and treatment categories, not on specific products unless they are especially well-known or relevant. In essence, LLMs interpret OTC health queries by explaining what the condition is and what types of remedies exist, often mentioning active ingredients or general product types, and only citing particular brands opportunistically for clarity or example.

Consider a few case examples:

• Acid Reflux (Heartburn): A user types: “I have bad heartburn – what can I take?” An LLM like ChatGPT will likely describe the major categories of OTC reflux medications. The answer might say, “Over-the-counter antacids can neutralize stomach acid for quick relief, while H₂ blockers (like famotidine) or proton pump inhibitors (like omeprazole) reduce acid production for longer-lasting relief.” It may even give examples: “common antacid brands include Tums or Rolaids, and an example of an H₂ blocker is Pepcid (famotidine).” Notice how the AI’s response is akin to a friendly pharmacist’s advice: it educates the patient about classes of remedies and their uses. It might mention one or two familiar brand names (e.g. Tums, Pepcid) to ground the explanation, but it won’t outright endorse a specific product unless prompted. The key point is that if your brand is an acid reflux remedy, it will only be mentioned if the AI has seen it referenced as a prominent example in credible sources. If you market “SuperSoothe Antacid” and that name isn’t recognized in medical content or data, ChatGPT is unlikely to volunteer it. The AI will stick to the well-trodden ground of known generics and brands, because its goal is to give a safe, broadly applicable answer.

• Cold & Flu: Now imagine someone asks: “What should I take for a bad cold?” A generative AI will typically break the answer into symptomatic relief: e.g. decongestants for a stuffy nose, analgesics for fever or aches, antihistamines for a runny nose, cough suppressants for a cough, etc. It might respond with: “For nasal congestion, a decongestant (like pseudoephedrine) can help; for fever and body aches, take an OTC pain reliever such as acetaminophen or ibuprofen; for a cough, consider a cough syrup with dextromethorphan.” This answer is comprehensive but generic – it educates the patient on types of medicines rather than pushing specific brands. Only well-known household names might get a mention in passing (for instance, the AI may say “acetaminophen (Tylenol)” or “ibuprofen (Advil)” as examples). If the user specifically asks for a brand recommendation – e.g. “Which is better, Tylenol or Advil for my cold?” – the AI will then weigh those specific options. But for a broad question about treating a cold or flu, the AI’s priority is to cover all relevant options fairly. It will reference your brand’s product only if it has become synonymous with that remedy (for example, NyQuil is often associated with nighttime cold relief, so an AI might mention “a nighttime cold medicine like NyQuil” if the context fits). Lesser-known brands or branded combinations won’t surface spontaneously.

• Pain Relief (Headache or Minor Pain): For a user query like “What’s the best remedy for a headache?”, a similar pattern follows. ChatGPT might answer: “For most headaches, an over-the-counter pain reliever is effective. You can try acetaminophen or an NSAID such as ibuprofen. Both are proven to alleviate headache pain – for example, acetaminophen is the active ingredient in Tylenol, and ibuprofen is found in Advil. It’s important to follow dosage instructions on the label, and if headaches are frequent or severe, consult a healthcare provider.” Again, the AI provides a balanced overview (mentioning two main drug options) and uses one or two famous brand names in an explanatory way. It does not enumerate every brand of pain reliever on the shelf. So if you are the maker of a newer or niche pain relief product, the AI won’t list it unless the question specifically involves it. The LLM’s neutral stance means brands must earn their spot in the answer by being part of the public medical discourse – either as ubiquitous household names or through inclusion in authoritative health content that the AI draws upon.

These examples demonstrate a crucial reality: LLMs answer health questions with generalized medical advice, not marketing. They interpret OTC categories by symptom and treatment type, aiming to educate. In doing so, they lean on information that is trusted, medically sound, and frequently mentioned across reputable sources. Any particular brand that features in the answer is there because it has effectively become a familiar example or a byword for its category (often through years of consumer usage and presence in literature). This means if your brand has not reached that level of recognition or integration into trusted content, the AI will likely omit it. As one industry observer noted, a generative AI will produce a single summarized response about, say, a “blocked nose,” explaining decongestant options and usage considerations – and unless your brand’s content is clearly identified as a trustworthy source on that topic, your product will be absent from that answer. In summary, LLMs handle consumer health queries with an emphasis on impartial, evidence-based information, which creates a high bar for brands to clear if they want to be organically mentioned.

Why Visibility Depends on Structured, Cited Data

In the age of AI-driven search, achieving visibility for your brand is no longer just about traditional SEO keywords – it’s about making your information easy for AI to find, trust, and use. Generative AI platforms don’t “rank” websites by links the way Google’s classic algorithm does. Instead, they synthesize answers from multiple sources, prioritizing content that is relevant, well-structured, and credible. The upshot is that how you present your content and data directly impacts whether an AI will include it in its answer.

Structure is critical. LLMs like ChatGPT interpret meaning from patterns in text. They find it easier to digest content that is organized logically – much like humans do. Utilizing clear headings, concise paragraphs, bullet-point lists, and FAQ sections on your site gives AI models explicit cues about what each section contains. For instance, a well-structured product FAQ page might have the question “What is the best way to take BrandX for heartburn?” followed by a straightforward answer. A generative AI scanning that page can quickly identify a relevant Q&A and potentially pull it into an answer if a user’s query matches. One best practice is to implement schema markup (structured data in your HTML, such as Schema.org tags) for health content – e.g. marking up a page as a “QAPage” or “MedicalWebPage” with identified questions/answers and medical terms. This provides clear signals to AI about your content’s focus. As a marketing technology firm noted, using structured formats like headers and schema not only helps human readers but “helps AI better understand and accurately present your content — critical for both citation in AI responses and rich results in traditional search.” In short, if your content isn’t structured for easy parsing, it might as well be invisible to an AI.

Citations and credible data are equally important. Large language models are trained on vast swaths of internet text, but modern AI search tools (like Bing Chat, Perplexity, and Google’s SGE) also actively retrieve up-to-date information and often display the sources. These systems have a built-in bias toward authority and trustworthiness – after all, when giving medical or health advice, the AI must avoid misinformation. Thus, content that demonstrates expertise and evidence stands a higher chance of being pulled into an AI’s answer. One key aspect is having cited, reference-rich content. For example, an article on your brand’s website about “managing acid reflux” that references clinical guidelines or includes a doctor’s review is more credible than a generic product page with marketing copy. Generative AI, much like Google’s algorithm, uses signals akin to Google’s E-E-A-T (Experience, Expertise, Authority, Trustworthiness) in determining what to trust. As ’s research highlights, content “backed by data, expert insights, and comparative analysis” – in other words, content with facts and citations to reputable sources – will perform better in generative search. Another industry analysis similarly notes that AI search engines favor “actionable, trustworthy content” and deprioritize generic fluff. If your brand’s pages are filled with vague claims or lack evidence, AI may either ignore them or even find contradictory authoritative sources to quote instead.

Transparency and accountability of your content can further boost AI trust. AI tools like Bing’s chatbot and Perplexity prefer to cite sources that have clear authorship and credentials listed. This makes sense – a health article authored by “Dr. Jane Smith, MD, Pediatrician” is more likely to be treated as reliable than one with no author or an unknown writer. Brands should therefore ensure that health guidance content (like blog posts, knowledge articles, FAQs) have bylines, author bios, and if possible medical reviewer information. These elements function as trust signals to the AI. As one SEO expert put it, “transparency builds credibility, not just with users, but with machines”. In practical terms, that means including a brief bio of the pharmacist or doctor who reviewed your “Cold & Flu Care Guide” on your site could make that page more likely to be surfaced or cited by an AI looking for authoritative info on cold remedies.

To illustrate why structured, cited data matters, imagine two scenarios. Brand A has a well-maintained online knowledge center where each OTC product has a page outlining what it treats, how to use it, evidence of effectiveness, and common questions – all formatted cleanly with headings (“Uses,” “Dosage,” “FAQ”) and with references to studies or guidelines. Brand B has a basic product page with a marketing blurb and no additional info. If a user asks ChatGPT, “How can I soothe a child’s cough?”, an LLM scanning its knowledge may recall information from sources like WebMD, the Mayo Clinic, and any high-quality content it was trained on. Brand A’s detailed FAQ might be similar in substance to what those authoritative sources say (and if it’s crawled or fed into Bing’s index, the AI might even retrieve it directly). Brand B’s page, however, provides nothing new or trustworthy for the AI to latch onto. The outcome: Brand A’s product (or at least the active ingredient in it) has a chance to be mentioned as part of the AI’s explanation, whereas Brand B is completely left out of the conversation. In essence, well-structured, well-sourced content is your ticket to participate in AI-driven answers. Without it, your brand’s perspective or benefits won’t be incorporated by the algorithm, no matter how great your product is.

Another dimension is the technical accessibility of your data. AI systems often have to fetch information in real time (for example, Bing’s AI will crawl pages to provide citations). They operate under time constraints – one report notes that a chatbot might only allow 1–5 seconds to retrieve content before formulating an answer. This means your website’s technical SEO must be solid: fast load times, no blocking of crawlers, up-to-date sitemaps, etc. If an AI can’t quickly get content from your site (or can’t interpret it because of heavy scripts or lack of indexable text), it will simply move on to another source. Using schema.org structured data helps here too, as it gives a quick summary to the crawler about what’s on the page. Some brands are even implementing a new idea called “LLMs.txt” – a directive file similar to robots.txt, which tells AI crawlers which pages are most important and how to use them. While experimental, it shows the lengths to which companies are thinking about making their content AI-ready.

In summary, earning visibility in AI-generated health advice requires playing by the machines’ rules: provide information that is easy to parse, grounded in facts, and carrying the imprimatur of expertise. Structure your content for machines and humans alike, and invest in the credibility signals (like medical reviews, citations, and schema markup) that generative models look for when deciding what to include in that all-important one-paragraph answer. Brands that embrace these practices will find that trust and visibility go hand in hand: as one health SEO expert succinctly said, “trust is currency in consumer health,” and AI engines will increasingly “prioritize reliable, authoritative sources.”

Roadmap: Auditing and Engineering Your Brand’s Presence in AI Search

How can OTC brands practically ensure they stay visible when patients ask AI for advice? The following roadmap outlines concrete steps for auditing your current AI search footprint and engineering a stronger presence in platforms like ChatGPT and Google’s generative search. This is a cross-functional effort – involving marketing, digital teams, e-commerce, and medical affairs – to optimize both content and technical aspects. Adopting these steps will help future-proof your brand in the era of AI-driven discovery:

1. Audit Your Brand’s AI Visibility: Begin by seeing what AI is saying (or not saying) about your brand today. Search for your products or related symptom queries on ChatGPT, Bing Chat, Google’s SGE, and other emerging AI tools. For example, ask ChatGPT, “What’s the best remedy for [symptom]?” or “Tell me about [Your Brand Name] cough syrup.” Note whether your brand or product is mentioned, and if so, how it’s characterized. Critically, look at any citations provided in AI-generated answers. These citations reveal which sources the AI is drawing from. If ChatGPT cites a WebMD article that mentions your competitor but not you, that’s a gap to address. Review the answers for accuracy and omissions. This audit establishes a baseline: you’ll discover if your brand is invisible in AI discussions, misrepresented, or overshadowed by alternatives. It can be eye-opening to see, in black and white, how an algorithm synthesizes your category. Share these findings with your team (especially Medical Affairs, to verify medical accuracy of any mention) and use them to prioritize where you need better content or outreach.

2. Optimize Content for AI (Structure and Semantics): The next step is to engineer your content so that AI algorithms can easily consume and trust it. Take the audit insights about common questions and make sure your brand provides compelling answers to those questions on your own channels. Update your website’s health content to be semantic and structured: this means organizing pages by intent, using natural language questions as headings, and providing clear, concise answers. Incorporate structured data markup like FAQ schemas, HowTo schemas, or Medical condition schemas where appropriate. For instance, create a FAQ section on your product page (“Q: Can I take this on an empty stomach? A: Yes, here’s why…”) – and mark it up with <script type="application/ld+json"> schema so Google and others recognize the Q&A format. Use bullet points or numbered lists to break down complex information. Not only does this improve user experience, it directly aligns with how AI extracts info. Think of each piece of content as a potential answer snippet. If your brand sells a pain reliever, you might publish a blog like “How to choose the right pain reliever,” structured with sections for different scenarios (headache, muscle pain, fever, etc.) and a comparison of acetaminophen vs. ibuprofen vs. aspirin. By addressing these specific pain points in a structured way, you increase the likelihood that an AI searching for “what’s best for muscle pain” will latch onto your content as part of its answer. This practice is essentially Generative Engine Optimization (GEO) – optimizing content so it appears in AI-generated answers. Remember, AI doesn’t care about keyword density; it cares about meaning and completeness. Cover the breadth of common user queries in your domain, and do so in a way that’s easy for an AI to parse and quote.

3. Bolster Credibility with Expert and Cited Content: Because AI models prioritize authoritative sources, you must demonstrate expertise and trustworthiness in every piece of content. Work with your Medical Affairs or qualified professionals to ensure all health guidance is accurate and up-to-date. Add author bylines and credentials to content – e.g., “Article by Dr. Jane Smith, MD, reviewed by John Doe, PharmD.” Having a human face and title on your content can significantly improve its credibility to an AI. Wherever possible, reference high-quality external sources (clinical studies, guideline documents, respected health websites) to back up your claims. For example, if your product is a nasal spray, cite what ENT specialists say about nasal spray usage, or reference the CDC guidelines for cold treatments if relevant. Proper citations and outbound links to .gov or .edu sites can signal that your content is not just marketing fluff. In the context of AI, think of it this way: you’re providing the model with verification that what you’re saying aligns with accepted knowledge. As noted earlier, content backed by data and credible citations “will perform better” in generative search. Additionally, keep your content updated. AI training data has cutoffs, but tools with browsing (like Bing/ChatGPT) will look for the most recent info, especially in medical topics. If there have been new recommendations (say, a decongestant ingredient was deemed unsafe for kids in 2025), ensure your content reflects that, or an AI might favor a competitor’s content that does. In summary, treat your content as if you’re writing a mini Wikipedia or WebMD entry about your product – thorough, unbiased, and informative. This investment in quality and integrity pays off by making your brand a trusted voice that AI will quote rather than ignore.

4. Leverage Authoritative Channels and Partnerships: Even as you enrich your own content, consider that AI models learn from the broader web, not just individual websites. To boost your brand’s prominence, it helps to be mentioned by name in authoritative publications and data sources. OpenAI has licensing partnerships with major publishers (e.g. Associated Press, The Atlantic, Reddit, and others) to feed their content into ChatGPT’s training. Google’s SGE also pulls heavily from sites that meet its E-E-A-T criteria (think high-quality health publishers, academic sites, government health pages). This means earned media and PR can directly influence AI visibility. If your new cold medicine is reviewed in a major news outlet or appears in a Healthline or MayoClinic article, those mentions become part of the corpus that AI might draw from. Seek opportunities to contribute expert content or quotes to reputable health sites and news articles. For example, have your medical director do an interview about “tips for managing heartburn” with a well-known magazine or website. Not only does that article lend authority to your brand, but if that outlet is one of ChatGPT’s content partners or simply ranks high on Google, the AI is more likely to have “seen” it. In essence, you want your brand embedded in the trusted knowledge ecosystem. Additionally, participate in structured databases if available – for instance, ensure your drug or supplement is listed (accurately) in medical databases like Drugs.com or in schema.org/health-lifesci datasets. If there are industry initiatives for sharing data with AI (some retailers or search engines might open up product feeds to AI), engage with them. The more legitimate touchpoints an AI has with your brand’s name tied to factual, useful info, the better. This is analogous to classic PR or link-building, but now you’re doing it for the benefit of AI algorithms as much as human referral traffic. As  suggests, getting your brand mentioned in authoritative news sources with formal AI content partnerships can “serve as strong signals of credibility and relevance” to the generative engines.

5. Optimize for Both Google and Bing (Technical SEO for AI): Maintaining traditional SEO hygiene is still very much necessary – not just for legacy reasons, but because AI search is built on the backbone of conventional search indices. ChatGPT’s browsing feature and many other LLM-based tools retrieve information via Bing’s search index. Google’s own AI summaries preferentially use content that ranks well (or at least is indexed) in Google’s engine. So, ensure your website is thoroughly indexed on both Google and Bing. Verify your Bing Webmaster Tools and Google Search Console to spot any indexing issues. It’s easy to focus on Google and forget Bing, but recall that “Bing underpins many generative engines, including ChatGPT”, so any SEO improvements on Bing can pay double dividends. Some specific steps: claim your business on Bing Places (for any local search relevance), keep your XML sitemaps updated and submitted, and address technical issues that could hinder crawling. Also, optimize site performance – as mentioned, AI bots might skip slow pages. Compress images, enable caching, and ensure your core web vitals are solid. Mobile-friendliness is key too, since many AI searches happen on mobile devices or via voice assistants. Another tip is to structure your site in content clusters: group your pages by topic so that if an AI finds one page, it can easily find the context (for example, link your “What is Acid Reflux” article to your “OTC treatments for Acid Reflux” page). This provides a rich network of relevant info that AI can crawl in one go. And don’t neglect the obvious: continue to target important organic keywords so that if a user does click out of an AI box for more info, your site is in the top traditional results. In Google’s SGE, the AI overview is often accompanied by a few source links – you want to be one of those links. Achieving that requires the same mix of good content and SEO that gets you on page 1 of Google. In short, think of GEO (Generative Engine Optimization) as an extension of SEO, not a replacement. By aligning with how LLMs pick up information and how search engines rank it, you cover all bases.

6. Monitor, Measure, and Adapt: The AI search landscape is evolving rapidly, so a successful brand strategy requires ongoing monitoring and agility. Set up systems to track your presence in AI answers. For instance, keep an eye on your web analytics for traffic from AI sources – in GA4 or other analytics, look for referrers like chat.openai.com, bard.google.com, bing.com/chat, or perplexity.ai. An uptick in traffic from these suggests your content is being surfaced by AI recommendations. There are also emerging third-party tools that attempt to measure brand mentions in AI outputs. These tools (some are in beta) can help you spot whether ChatGPT or others are mentioning you or a competitor more frequently, though they may not be fully reliable yet. In the meantime, nothing stops you from periodically spot-checking AI results manually – the same way companies used to do Google rank checks. For example, each month, have your team run a set of important queries on the major AI platforms and document the results. If you notice, say, that your brand was cited in a ChatGPT answer one month but not the next, investigate why – perhaps a competitor published new content that the AI favored, or maybe an update to the AI’s model changed its sources. Treat AI visibility like you would treat SEO rankings: as key metrics to watch. Also stay informed on new developments: if Google expands SGE to more scenarios, or OpenAI releases tools for publishers, be ready to adapt your strategy. The goal is to build a feedback loop: use monitoring to identify where you’re winning or losing, and then refine your content or technical approach accordingly. Over time, you’ll develop an intuition for what “AI-friendly” content looks like in your niche. Keep in mind that although universal metrics for “AI share of voice” are nascent, you can still gauge impact through indirect measures – like increases in branded search volume (if more people see your brand in AI answers, they might search your name after), referral traffic, or even conversions that follow an AI interaction. Be prepared to pivot; what works to get mentioned on ChatGPT today might need tweaking in a year as these models get smarter.

7. Innovate with AI Integration: As a forward-looking consideration, explore ways to integrate directly with AI platforms. This step goes beyond content optimization into offering new AI-driven experiences for consumers. For example, some brands are beginning to create ChatGPT plugins or custom AI assistants related to their product domain. An OTC brand might develop a plugin that ChatGPT users can enable to get official dosing recommendations or product finding for that brand. OpenAI has opened a plugin ecosystem, and being an early mover there could position your brand as a default resource for relevant queries. Similarly, consider training a custom chatbot on your own content (OpenAI’s APIs or Azure AI services allow fine-tuning models on proprietary data). A consumer might ask Alexa or Google Assistant for advice, and your brand’s custom AI skill could provide a trusted answer that mentions your products. While these integrations are still emerging, they hint at a future where brands can supply verified information straight into the AI channels consumers use. Even if you’re not ready to build a custom AI, ensure your e-commerce or product data is AI-accessible. For instance, if Google’s AI can directly surface a “buy now” option, you want your feed in Google Merchant Center to be accurate and your product descriptions AI-optimized. OpenAI’s recent move to enable direct product purchases within ChatGPT (with early partners like Shopify stores) shows that transactions may eventually happen right inside chat interfaces. It’s not far-fetched that a user could ask, “ChatGPT, order me some Acme Allergy Relief,” and the AI will complete the purchase. Brands need to be technically ready for that future by collaborating on integration points that tech companies offer.

By following this roadmap – auditing where you stand, optimizing content structure and credibility, strengthening your presence in authoritative sources, keeping up with SEO fundamentals, monitoring outcomes, and embracing new AI opportunities – your OTC brand can secure a place in the AI-guided consumer journey. The overarching theme is to ensure that whenever an AI “thinks” about your category, it cannot ignore your brand. That only happens if you’ve systematically seeded your brand’s expertise and value proposition into the fabric of the web and data that these AI models draw from.

Conclusion

The age of “AI search” in healthcare is no longer theoretical – it’s here. Consumers are asking ChatGPT and similar AI agents about everything from sniffles to chronic condition management. In this environment, OTC brands face a pivotal challenge: if the AI doesn’t mention you, for many consumers it’s as if you don’t exist. This makes optimizing for AI-driven discovery as mission-critical as SEO became in the 2000s. The encouraging news is that the core principle remains the same: deliver genuine value through information. Brands that invest in structured, authoritative content and digital best practices will find that AI can actually deepen their connection with consumers by providing helpful answers at the exact moment of need. On the other hand, brands that neglect these changes risk losing visibility to competitors or, worse, to misinformation.

For Chief Marketing Officers and digital leaders, now is the time to expand your search strategy to encompass AI platforms. Break down silos between SEO, content marketing, and medical affairs – generative AI cares about the total quality of information, not which department produced it. Build an “AI visibility” audit into your routine, much like you review Google rankings or sales metrics. Advocate for resources to update content and add the metadata or schema that machines require. Involve your medical and regulatory teams early so that any content enhancements maintain compliance and accuracy (critical in health contexts). Educate your organization that appearing in an AI’s answer box is the new battleground for brand awareness.

Crucially, embrace the idea that AI search isn’t replacing your existing efforts, but augmenting them. Traditional search, e-commerce, social media – all still matter. AI is an additional layer where consumers can either encounter your brand or not. By following the roadmap outlined here, you can ensure that when patients turn to ChatGPT or Google’s AI for help, your brand’s message is part of the conversation. In doing so, you not only safeguard your visibility – you also reinforce your brand’s authority and trust in the eyes of consumers and algorithms alike. The companies that move now to optimize for AI-driven search will build an early lead, turning this technological disruption into a growth opportunity. After all, the fundamental goal hasn’t changed: to be there for your consumers when they seek answers. The medium of discovery may be shifting from typed queries to spoken questions to AI, but with the right strategy, your brand can continue to shine as a helpful, relevant, and visible guide in every patient’s health journey.