The AI Health Funnel: From ChatGPT Query to Pharmacy

AI-driven assistants like ChatGPT, Perplexity or Amazon’s new Rufus have rapidly become the new “top of funnel” for consumer shopping, especially in health and pharmacy categories. Recent surveys show roughly half of consumers now turn to generative AI for product research (59% of U.S. adults; 55% of UK adults). In the UK, nearly 4 in 10 shoppers prefer ChatGPT over Google for shopping queries, and 26% say its recommendations beat Google’s. AI assistants are already “increasingly the trusted guides” for healthcare decisions, reshaping the funnel so that brand discovery begins with a conversational query, not a search engine or supermarket shelf.

  • AI as Discovery Gatekeepers: As Azoma CEO Max Sinclair observes, “AI shopping assistants like Amazon’s Rufus and ChatGPT are not just new channels – they are gatekeepers. They determine which brands are recommended, which products are visible, and ultimately where demand flows”. In other words, visibility in AI results now directly impacts retail demand for OTC products. If AI cites your brand in its answers, you meet the customer at the exact moment of intent; if it cites competitors, your market share can erode even if your Google or Amazon ranking is strong today.

  • AI Top-of-Funnel Impact: Generative AI is dramatically compressing the purchase journey. Industry data show that AI overviews (chatbot answers) have already begun “decimating” traditional search click-throughs: Gartner predicts organic search traffic could drop 25% by 2026 as AI assistants answer queries directly. In practice, shoppers now conduct much of their comparison and decision-making inside the AI interface. Azoma’s analysis found that ChatGPT referrals convert at ~14%, nearly five times the 3% conversion of Google Search traffic – meaning users who click through from AI queries are already very far down the funnel (ready-to-buy prospects). In short, AI assistants are not just changing how consumers search – they are shifting where the funnel starts and how purchases materialize.

AI Recommendations and Brand Preference

AI assistants do more than retrieve products; they curate and compare based on intent, which can shape brand preference in subtle ways. Unlike keyword-based search, AI recommendation layers interpret context, trust signals and content quality to surface brands. As LiveWorld’s AI-OTC report notes, “AI visibility drives consumer preference”: brands with strong digital ecosystems and consistent AI presence (like CeraVe, Neutrogena, Advil) scored highest. In particular, AI models tend to favor brands with trusted storylines or perceived benefits: legacy names (Pepto-Bismol) or “natural” brands (Nature Made) are frequently surfaced in answers. Conversely, even top-selling brands can lose ground if their digital signals are fragmented or outdated – a low “AI footprint” means missed visibility and opportunities.

AI assistants leverage multiple layers of information when choosing products:

  • Structured Product Data: ChatGPT and others rely on product metadata and schema (price, category, attributes). Well-tagged products (complete descriptions, attributes, FAQs) are more likely to be understood and recommended. For example, Boots/CVS product listings should fill every attribute field (dosage form, age range, ingredients, etc.) so AI can parse the details.

  • User Reviews & Context: Detailed customer reviews – especially those that provide use cases, comparisons and emotional context – give AI rich signals of product satisfaction. ChatGPT shopping pulls information from sources like Amazon reviews, Reddit threads, and Q&A pages to inform its suggestions. Brands that foster high-quality reviews (including prompts for context or benefits) feed the AI’s understanding of their products.

  • Trust & Authority Signals: AI assistants implicitly weigh credibility. As LiveWorld found, brands that engender consumer trust and satisfaction often outperform bigger-budget rivals in AI answers. This means heritage and authenticity matter. Marketers should ensure positive brand narratives are prevalent on authoritative sites and forums – these become the “citations” AI uses to craft answers.

In essence, AI recommendation layers are rewriting brand preference: it’s no longer enough to bid on keywords or buy search ads. Now you’re competing to be part of the answer. The brands that provide the richest, most trustworthy content – from structured data to authentic reviews and expert mentions – will emerge as preferred choices in an AI-curated shopping journey.

Mapping AI Visibility to Boots/Amazon Conversions

Because AI is reshaping discovery, brands must ensure the path from AI to purchase is seamless. When an AI assistant recommends a product, that recommendation should translate into a sale on the retailer’s site or in-store. Several developments illustrate this bridge:

  • AI-Assisted E-Commerce Integration: OpenAI has integrated shopping into ChatGPT. Its “Instant Checkout” feature (via partnerships with Shopify, Etsy, Stripe, etc.) lets users browse and buy products within the chat window. In practice, a shopper can ask ChatGPT, “What’s a good skincare set for under $50?” and immediately purchase one of the AI-suggested products without leaving the conversation. This means AI discovery can flow directly into an eCommerce transaction – bypassing traditional search pages entirely. Brands selling through Shopify/Etsy (or soon Walmart, etc.) can plug into this system so AI recommendations send shoppers right to their checkout.

  • Amazon’s AI Assistant (Rufus): Amazon has introduced Rufus, an in-app AI shopping assistant trained on Amazon’s vast catalog and reviews. Rufus acts like a knowledgeable sales associate: it answers questions (e.g. “Best cold medicine for a toddler?”) by pulling data from product listings, reviews, and Q&As. For OTC brands, this means that the way a product is presented on Amazon (complete details, clear benefits, high ratings) directly influences whether Rufus will recommend it. Brands should optimize their Amazon pages accordingly – use enhanced content, fill out all specs, include helpful Q&As, and even design infographics with key text (“24-hour relief”, “pharmacist-tested”, etc.) since Amazon’s AI can read image text. When Rufus (or other AI tools) suggests a Boots or CVS private-label item, the shopper can click through to the product page and buy it, closing the loop from AI recommendation to sale.

  • Conversion Multiplier: Evidence suggests AI-driven referrals are far more likely to convert. Azoma’s analysis of dozens of sites found ChatGPT-sourced visitors had a ~14% conversion rate—nearly five times higher than Google Search traffic (3%). Similarly, Adobe reports AI-referred shoppers exhibit stronger engagement (longer sessions, more pages per visit) and have almost caught up in conversion rate (AI visits were only 9% less likely to convert than non-AI by early 2025). In practical terms, when an AI assistant includes your product in its answer, it is like sending a highly qualified prospect to that product page. For OTC brands, this means AI visibility can directly drive sales – whether on Amazon, Boots.com, or other retailers.

  • Boots and Retailers: Traditional retailers are taking notice. In the UK, Boots is reportedly developing a ChatGPT “personal shopper” for its website. While details are limited, this underscores how retail chains intend to link AI guidance with their own product offerings. Brands sold at Boots should prepare by ensuring Boots.com listings and Boots-specific content (FAQs, blogs) are optimized for AI bots. Every prompt about cold medicine, vitamins or skincare that ends in “Go to Boots” is an opportunity.

In summary, mapping AI visibility to retail conversions means treating AI assistants as an official marketing channel. A mention in ChatGPT should translate to a footprint on your eCommerce (or in-store) statistics. By optimizing for AI discovery (below), brands can capture that high-converting traffic.

Optimizing Product Data for AI-Linked Discovery

To win in the AI health funnel, brands must treat product content and data as their new battleground. The AI “decision engines” thrive on structured, rich information. Key optimization strategies include:

  • Structured Data and Schemas: Use schema.org and other metadata to label product attributes (name, category, dosage, active ingredients, etc.). Structured markup helps AI parse your content. Azoma advises adding schema tags on product pages so AI tools can accurately interpret elements like prices, shipping, and specifications. On marketplaces like Amazon, complete every backend attribute (formulation, strength, uses, age range, etc.), because assistants like Rufus use these fields when answering questions.

  • Rich, Contextual Product Descriptions: Move beyond bare facts. Tell a narrative that answers shopper questions. Describe why a product helps, who it’s for, and how it’s used. AI systems perform better with context-rich text. For example, if your vitamin brand has a strong “trusted history” angle (like Nature Made’s “natural ingredients”), spell out that story. Use bullet points and FAQs to anticipate queries (e.g. “Are these tablets sugar-free?”). The more natural language you provide, the more AI can find relevant content to cite.

  • Review-Driven Content: Turn user reviews into an AI asset. Encourage customers to write detailed reviews focusing on use cases and outcomes. Prompt them for comparisons or scenarios (e.g. “How did this allergy medicine work for your seasonal sniffles?”). Then showcase these reviews prominently. Some brands create dedicated review landing pages or syndicate reviews to forums; ChatGPT and others scrape these opinions. Azoma and others suggest “centralizing product reviews” so AI can index them. High-quality reviews act as trust signals and use-case libraries for AI answers.

  • Fresh, Accurate Product Feeds: Ensure all online listings have up-to-date inventory, pricing, and availability. Stale data can sink AI visibility – if an assistant finds outdated or inconsistent info, it may drop a product. Notably, OpenAI now lets merchants submit live product feeds to ChatGPT to guarantee inclusion. Brands should enroll in such programs (and those of other AI platforms) to feed their catalogs directly into AI training data.

  • Platform-Specific Enhancements: Tailor data for each retail channel. On Amazon, leverage all media slots: high-res images of packaging (AI reads text on the label), infographics with benefit callouts (parsed by Amazon’s AI), and A+ Content sections with narratives and FAQs. On Boots.com or others, use structured FAQ sections. For example, adding Q&A pairs on your site not only answers customers but also provides AI with crisp factoids (e.g. “Is this safe for children? – Not under age 12” becomes AI-usable data). Every platform’s structured content (HTML tables, alt text, bullet lists) should be fully utilized.

  • Monitor and Update Frequently: AI visibility is dynamic. Use AI analytics (e.g. Azoma’s platform or other tools) to see which queries mention your brand. If you find blind spots (relevant questions not covered by your content), create new pages or update FAQs. Traditional SEO tactics apply, but with AI in mind: answer longer-tail, conversational questions (“best OTC remedy for sore throat at night”) and ensure those answers are crawlable on your site or syndication channels.

By treating product data as fuel for AI recommendation engines, OTC brands can maximize their chances of being surfaced. The goal is to make your products transparent and compelling to machines as much as to humans.

KPI Framework: Measuring “AI Share of Recommendation”

As with any marketing channel, what gets measured gets managed. Brands and retailers should build metrics around AI-driven discovery. Some key KPIs and measurement ideas include:

  • AI Share of Recommendation (AI-SOR): Inspired by emerging industry ideas, AI-SOR is the percentage of relevant AI queries that recommend your brand. For example, if you track common health-related prompts, what share produces your product name? This is analogous to “share of voice” for AI. While still conceptual, it could be measured via tools that query ChatGPT/Bing/etc. on a list of target topics and record brand mentions.

  • AI Referral Traffic & Conversion: Track how much site traffic comes from AI sources. In Google Analytics, look for chat.openai.com, bing.com/chat, perplexity.ai, etc. as referral sources. Report AI traffic as its own channel. Measure conversion rate of AI referrals vs. other channels (as Azoma did). Rapid increases in this metric signal growing importance. Likewise, attribute sales or leads back to AI where possible (e.g. ask customers “How did you find us?”).

  • AI-Assisted Sales: For marketplaces, monitor how many sales originate from features like ChatGPT integrations or affiliate links from AI assistants. If using the ChatGPT Instant Checkout, tag purchases as AI-driven. For Boots or retailers, use UTM codes or custom affiliate IDs that AI interactions might generate. While still nascent, experiment with tracking codes that capture whether a customer came via a chat link or plugin.

  • Content Visibility Audits: Periodically check AI results for key queries. Note where your brand appears. This can be manual (“chat with AI: ‘What’s the best headache medicine?’”) or automated via APIs. Track changes over time: e.g. an “AI Visibility Score” from 0–100 based on presence in top answers for your product category queries.

  • Engagement Metrics: As Adobe’s research shows, AI-referred visitors tend to engage more deeply. Use these engagement signals as proxies for success. Are AI users spending more time, viewing more pages, or adding to cart at higher rates? If so, double down on that content.

  • Brand Mention and Citation Count: Use digital PR metrics to count how often your brand or products are cited in authoritative content (health sites, Reddit discussions, news articles). AI assistants heavily draw on such citations. A rising share of brand mentions in AI-source content suggests growing AI-friendliness.

  • AI Trust and Sentiment: Since trust is still a barrier (32–37% of shoppers worry about AI losing the human touch), monitor customer sentiment on AI platforms. Tools like social listening or feedback forms on AI queries can reveal if your AI-related interactions are building or eroding trust (e.g. misinfo, tone). For example, if you implement a site chatbot, track satisfaction scores.

Implementing an AI-focused analytics dashboard is wise. Start by adding “AI traffic” as a channel, and set initial benchmarks. Gartner’s and Azoma’s predictions stress we’re at a tipping point: today’s AI visits may be small in number, but they are high-value and growing explosively. Establish monthly targets (e.g. 10% of new traffic from AI sources) and “AI SOR” goals (e.g. brand mentioned in 30% of category queries). Over time, these KPIs will clarify how much revenue and engagement you earn through AI versus legacy channels.

Conclusion

The rise of AI assistants is fundamentally reshaping the OTC health funnel. Where once Google search was king, now ChatGPT-like interfaces and retail-specific bots (Boots’ plans, Amazon’s Rufus) own the starting point of many health purchases. For eCommerce directors, category managers and retail partners, the imperative is clear: treat AI like any major media channel. That means optimizing product data, reviews, and brand content for AI “consumption,” and then measuring how often AI recommends you and drives sales.

In practice, brands should audit their AI visibility footprint: Are you present on health forums, news sites and Q&A pages that AI reads? Are your Amazon/Boots listings fully fleshed out with the information Rufus or ChatGPT needs? By engineering an AI-friendly digital presence, brands can capture the high-intent shoppers AI generates. After all, studies show AI users often skip websites – the AI response is their first impression of your brand. Make sure that first impression positions you at the front of the health shelf, whether virtual or physical.

Dr GPT, Dr GoogleFrancesca Tabor