The AI Shelf: Competing for Space in ChatGPT’s Product Recommendations
Executive Summary
AI assistants like ChatGPT and Perplexity are rapidly becoming the new “retail shelf” for consumer product discovery. Rather than scrolling through pages of search results, consumers now ask AI agents for recommendations and receive a single, trusted answer. For beauty brands, this shift presents a transformative challenge and opportunity: securing placement in that solitary AI-generated recommendation. Data from Similarweb already shows that one in five of Walmart’s referral clicks in August 2025 came from ChatGPT, with similar spikes for retailers like Etsy and Target. And an estimated 60% of U.S. consumers have used generative AI for shopping help, indicating that AI-driven shopping is becoming mainstream.
This whitepaper explores how brands can win visibility and trust on the “AI shelf.” It details the strategies beauty brands must adopt to have their products chosen by AI algorithms: providing comprehensive structured data about products, cultivating authoritative citations across the web, and embracing ingredient-level transparency. By optimizing these factors, brands can ensure that when a user asks an AI, “What’s the best serum for my skin?”, their product is the one that surfaces as the credible, data-backed recommendation. The following sections outline the evolution from traditional e-commerce to AI-driven commerce, explain how AI recommendation algorithms work, and provide a blueprint for leveraging data quality and authority to secure prime placement on the AI shelf space – ultimately transforming how brands achieve visibility and consumer trust in the era of conversational shopping.
From E-Commerce to AI Commerce
In the past, winning online meant ranking on the first page of search results or securing eye-level placement on a digital storefront. Today, those paradigms are shifting. The most critical moment of influence now happens upstream – in AI-driven answers and “zero-click” searches – before a shopper ever sees a product listing or website . Consumers are increasingly posing questions directly to AI assistants (“Which shampoo should I buy for dry scalp?”) and getting an immediate recommendation, bypassing the traditional multi-click browsing journey. Nearly 60% of all searches are now zero-click, where the user’s query is answered directly without any further site navigation . Moreover, AI-powered tools are converting users at higher rates – about 1.7× the conversion of traditional Google search – suggesting that when consumers get a tailored answer from an AI, they act on it with confidence.
Enter the “AI Shelf”
This new paradigm can be thought of as the “AI shelf.” Just as products at eye level in a store or top results on an e-commerce site enjoy the most attention, the product an AI assistant names first in its answer becomes the de facto top shelf item. Imagine a shopper asking ChatGPT or Google’s Bard which moisturizer to buy – the first brand surfaced is often the one that wins consideration, and if your brand isn’t mentioned at all, you’re essentially invisible in that decision moment. Unlike a physical shelf, however, the AI shelf is not static or easily observable; it’s a dynamic, context-dependent answer generated anew for each user query. In fact, the AI’s recommendation is:
Invisible and dynamic: It’s not a permanent listing but an ephemeral answer that can change with phrasing or user context. Your product might appear one minute and be replaced by a competitor the next if the question is worded differently.
Emotionally weighted: The tone and language of AI responses influence perception. An AI’s confident, positive recommendation can imbue trust, whereas a hesitant tone could sow doubt. Brands have no packaging or visuals here – the sentiment of the AI’s wording becomes the packaging.
Platform-driven: Placement on the AI shelf is decided by algorithms, not retail buyers or paid slotting fees. There’s no direct paying for “end caps” in an answer engine; visibility must be earned through relevance and authority.
For marketers, this means traditional methods of buying visibility (SEO keywords, paid ads, eye-catching packaging) are no longer sufficient. Visibility in AI-generated answers has to be earned by aligning with the AI’s selection criteria. The implications are profound: if your brand doesn’t show up in these AI-curated answers, you may never even enter the consumer’s consideration set. As shoppers bounce between social media, voice assistants, and AI chatbots in a non-linear journey, the AI’s first answer can effectively make the sale before a customer ever visits a website or store.
In summary, AI commerce compresses discovery-to-decision into a single exchange. With ChatGPT already driving over 20% of referral traffic to some retail sites and AI queries like “recommend a good laptop under $1000” becoming commonplace, it’s clear that the AI shelf is the new battleground for brand visibility. Brands must now focus on being the trusted answer an AI provides, rather than just one of many links on a search results page.
Understanding the AI Shelf Algorithm
What determines which product an AI assistant picks as the top recommendation? In place of a physical shelf’s planogram or a search engine’s familiar ranking factors, we have a complex algorithmic scoring system. Instead of human retail buyers or bid-based ad ranks, AI models evaluate products based on relevance, data, and inferred trust. While proprietary AI algorithms (like those behind ChatGPT’s shopping mode or Perplexity’s answers) are opaque, industry analyses suggest some key ranking signals:
Relevance to the query: First and foremost, the AI tries to match the user’s intent. It parses the question for needs or preferences (e.g., “hydrating cleanser for eczema-prone skin”) and looks for products whose descriptions, ingredients, and use-cases align closely with those terms . A product explicitly labeled for “eczema-prone, dry skin” with ingredients known to help that condition will score higher for that query than one with a generic description .
Product performance & sentiment: AI systems often have access to aggregate performance data or at least proxies for it. High average ratings, positive review sentiment, and even low return rates can serve as indicators of a product’s quality and popularity . A moisturizer with thousands of 5-star reviews is a safer bet for the AI to recommend than one with mixed feedback.
Data structure and quality: How well a product’s information is structured and annotated significantly impacts AI selection . If your product catalog is properly categorized, uses consistent tags (e.g. “vegan, cruelty-free, contains vitamin C”), and includes schema markup (more on that in the next section), the AI can more easily “understand” and retrieve it. A well-structured product entry is essentially more legible to the algorithm.
Prior engagement or conversion history: While still an emerging factor, some AI shopping algorithms might learn from user interactions. If a product frequently gets clicked or purchased when recommended (i.e., it has a strong conversion rate when shown), the system may be more likely to recommend it in future similar queries . High-performing products tend to maintain priority in future results – success begets success in algorithmic eyes.
Personalization signals: Modern AI assistants can tailor results to the individual when data is available. This could include the user’s past purchases, browsing history, location, or stated preferences . For example, if the AI knows a user often buys clean beauty brands or has an allergy noted in their profile, it will favor products matching that profile. The “shelf” reorders itself in real-time for each user.
These factors combine into an ever-shifting, user-specific ranking – an AI shelf that looks different for each shopper and each query . Importantly, many of these signals are derived from the information that brands themselves provide (or that others provide about them). If the underlying data about a product is incomplete or not aligned with what the AI is looking for, even a fantastic product can remain hidden. As one expert put it, “many excellent products are buried… not because of poor performance – but because they’re misaligned with AI systems”. In other words, if you haven’t described and positioned your product in a way that AI algorithms can easily interpret, you essentially don’t exist on the AI shelf.
It’s also crucial to recognize the paradigm shift for brand marketers and merchandisers. The craft of earning AI recommendations is a multidisciplinary challenge – part data science, part content strategy, part technical SEO. Leading brands are responding by building AI-focused merchandising teams that treat AI algorithms almost like a new class of retailer. These teams work on prompt engineering (to test how and when their products are suggested), on metadata and schema optimization, and on analytics to track AI referral performance. As Francesca Tabor notes, “Brand managers must now think like engineers. Product visibility depends less on design and more on digital structure and semantic alignment.” Winning a spot on the AI shelf requires speaking the AI’s language. In the next sections, we’ll delve into how exactly a brand can do that – starting with the foundation of high-quality structured data.
The Role of Data Quality and Schema Compliance
To get recommended by an AI, a product must first be seen and understood by that AI. This makes data quality and schema compliance the bedrock of AI shelf visibility. In practice, this means providing product information in a structured, standardized way that AI systems can readily ingest and trust.
1. Structured data as the foundation: Today’s AI shopping agents, from ChatGPT to Google’s generative search, heavily rely on structured product data provided by merchants. OpenAI explicitly asks merchants to share a Product Feed — a structured catalog of products — so that ChatGPT can accurately index and display up-to-date product info. In OpenAI’s own words, “ChatGPT relies on merchant-provided feeds” as a source of truth for pricing, availability and other details. Similarly, Google’s AI (Gemini) is expected to draw from Google Merchant Center feeds, and Bing’s AI from Bing Merchant integrations . Brands should ensure they are part of these feeds. If you sell online, submit your product data to OpenAI’s merchant program for ChatGPT (an early access program for shopping partners ) and keep your Google/Bing product feeds updated. This guarantees the AI has your latest product info straight from the source, rather than scraping unreliable third-party data.
2. Don’t block the crawlers: Beyond feeds, AI assistants also crawl the web for information. OpenAI’s own web crawler (OAI-SearchBot) and others need access to your site. If your website’s robots.txt or meta tags block these bots, your products become invisible by default to AI. A quick audit to ensure that AI crawlers (OpenAI’s, Bing’s, etc.) are allowed to index your product pages is critical. Every product detail page (PDP) should be crawlable. Brands that accidentally left parts of their site off-limits to bots may find their products omitted from AI answers simply because the AI couldn’t “see” them.
3. Embrace schema markup and standards: Using standard schema (JSON-LD) markup on your product pages is no longer just about SEO for Google – it’s about being understood by AI. OpenAI, Google, and others increasingly parse schema-structured data to identify product attributes . Ensure your site’s code includes structured data for all key fields: product name, description, price, brand, availability, ratings, etc. Many modern e-commerce platforms (like Shopify) have this by default, but always verify with tools like Google’s Rich Results Test. Data alignment matters: the AI will cross-verify information, and inconsistencies (e.g., price mismatches between your feed and site) could downgrade trust in your product. Providing consistent, structured data across all channels creates a coherent picture of your product for the AI.
4. Complete and granular information: Data quality isn’t just about format, but also depth. The more complete your product info, the better. Include detailed specifications and attributes. In beauty, this especially means ingredient-level transparency. For instance, if you have a skincare product, list out key ingredients and their benefits in structured data (there are schema types for product ingredients and benefits). Applying schemas for ingredients, skin concerns, certifications, etc., makes your product far more discoverable for specific queries. Consider a user asking, “Which moisturizer contains hyaluronic acid and is fragrance-free?” If your moisturizer’s data explicitly flags “hyaluronic acid” and “fragrance-free” (and perhaps has a schema tag for suitable skin types or concerns), the AI can recognize its relevance. Transparency also builds trust: a recent industry report found that 81% of Gen Z consumers value ingredient transparency in beauty products – openly sharing full ingredients and even certifications (like cruelty-free or organic labels) can boost the AI’s confidence and the consumer’s confidence in recommending your brand .
5. Keep data fresh and error-free: AI recommendations are only as good as the data behind them. It’s crucial to maintain real-time accuracy. If your stock runs out or price changes and your feed isn’t updated, the AI might drop your product for an alternative that has up-to-date info. (Nothing will erode an AI’s trust faster than recommending an out-of-stock item.) OpenAI’s system, for example, accepts feed updates as frequently as every 15 minutes. In general, treat your product data like a living thing – update descriptions, refresh inventory and price info, and correct errors promptly. Quality control on data is key: typos, inconsistent units, or missing fields can all confound an AI. Tools now exist that can scan for “gaps” like missing attributes, stale prices, or weak schema on your listings . These gaps, if left unchecked, act as blockers that can knock your product out of contention on the AI shelf.
In essence, think of structured data as the packaging and barcode for the AI shelf. If the packaging is damaged (poor data quality) or the barcode is missing (no schema), the product won’t get stocked in the AI’s inventory of knowledge. As Goodie’s AI commerce report succinctly put it, brands need complete, consistent, and compliant product data everywhere an AI agent looks. By investing in excellent data hygiene and schema compliance, you create the foundation on which all other AI shelf strategies build. Your product becomes “AI-ready” – positioned to be picked up by the algorithms rather than passed over due to technical oversights. With this foundation in place, we can turn to the next layer: building the authority and citations that make your product not only visible, but credible to the AI.
Citation Power: How Authority Drives Inclusion
If structured data is the foundation, authority is the magnet that pulls the AI’s attention. AI assistants don’t operate in a vacuum – they draw on vast corpora of web content and often cite external sources to justify their answers. In fact, many generative search engines (like Perplexity and Bing’s AI chat mode) will explicitly show the sources they used. This means that the content and context surrounding your brand across the web can directly influence whether and how your product is recommended.
Two dynamics are at play here:
1. The AI trusts what trusted sources say. Large language models have essentially read the internet. When asked for a recommendation, they will recall or retrieve information from sources they consider reliable and relevant to the query. A recent analysis of AI citations in the beauty domain revealed a striking insight: it’s not usually the brands’ own websites that AI models cite, but a concentrated network of third-party “trusted” domains . In beauty, for example, forums like Reddit, retailer sites like Sephora (with its community Q&A and reviews), and editorial sites like Byrdie or Allure are among the most frequently cited sources in AI answers. Over a third (36.7%) of all beauty-related citations by AI models went to just the top ten domains (those major forums, retailers, and magazines) . If your brand or product isn’t present on those domains, you’re largely absent from the AI’s knowledge. LLMs “learn” which sites are authoritative by how often they appear and in what context. Thus, a mention of your serum in an Allure article titled “Best Hydrating Serums of 2025” or in a highly upvoted Reddit thread on skincare can dramatically increase the likelihood that ChatGPT will recommend it when asked about hydrating serums.
2. Earning a mention is the new SEO. In the era of the AI shelf, getting your brand mentioned by credible sources is as important as, if not more than, traditional SEO keywords. We can think of this as Answer Engine Optimization (AEO) or Generative Engine Optimization. It’s about shaping the information landscape so that, when an AI combs through it, your brand features prominently. This can be achieved through a few tactics:
Cultivate authoritative content and reviews: Encourage and where possible facilitate reviews on retailer sites (Sephora, Ulta, Amazon if accessible, etc.), since those are fodder for AI. Pitch your products for inclusion in editorial “top picks” lists on trusted publications. Even sponsoring or collaborating on expert articles can help – e.g., a dermatologist blog that discusses the efficacy of your product’s key ingredient. Remember, LLMs rely on a network of trusted domains ; getting your brand into that network is key.
Publish high-value content on your own channels too: While your site might not be the first thing AI trusts, it still matters if you provide truly useful information. For instance, a detailed blog post on your site comparing different types of vitamin C in skincare could be picked up by Bing’s index or cited by another site. Some AI models will surface content that directly answers a user’s question (regardless of source). If you become the source of a well-cited explanation or guide, that indirectly boosts your brand authority. Creating “pillar” content that thoroughly answers common consumer questions in your category (e.g., “How to choose the right serum for dry skin”) can position your site as a source that AI might pull from . Optimize this content for AI parsing: use clear language, structured headings, and even Q&A formats so the AI can easily digest it.
Secure expert endorsements and third-party validation: Authority is not just technical; it’s also experiential. If your product is recommended by dermatologists or has clinical studies published, ensure those are public and referenced online. AI models trained on medical or scientific texts will recognize such endorsements. Citation power can come from academic papers, news articles, or industry awards. These lend an extra layer of credibility that pure marketing content can’t achieve.
The ultimate goal is to build a web of credible citations around your brand. When an AI scans its memory or the live web for “best solution for X problem,” your brand should appear in the supporting evidence. Brands that ignore this essentially let their fate be decided by whatever content already exists – which might be sparse or neutral at best, or could even be negative. On the other hand, brands that strategically seed and foster positive, informative mentions reap the rewards. It’s akin to the old PR adage “get people talking about you,” but here it’s “get the right people and platforms talking about you, so the AI listens.”
Monitoring which sources AI platforms are citing is instructive. Share-of-voice in AI can be measured by looking at, for example, Perplexity’s sources for queries in your category or analyzing which blogs and sites appear in Google’s AI snapshots for those queries. If you find you’re absent, it’s a sign to ramp up content efforts on those platforms. As Quirk’s Media notes, it’s about identifying the domains AI draws from frequently and ensuring your brand is represented there . This might involve proactive outreach – e.g., providing samples to journalists or influencers who contribute to those sources, or contributing expert commentary that gets quoted.
Finally, maintain E-E-A-T (Experience, Expertise, Authority, Trust) in all your content creation . AI models (and the search engines that feed them) are increasingly tuned to surface content that demonstrates expertise and trustworthiness – a direct result of initiatives to combat misinformation. By publishing transparent, well-researched, and helpful content (and avoiding overly promotional or thin material), you increase the likelihood that your brand’s mentions are treated as authoritative. In practical terms: a well-written FAQ on your site answering “Is ingredient X safe for sensitive skin?” that cites dermatological research could be both a direct citation source and a trust signal.
In summary, citation power is about playing the long game of brand information equity. It’s ensuring that across the internet, your brand is portrayed as a credible solution to the problems your products solve. When the AI gathers answers, this robust citation presence acts like a gravity well, pulling your brand into the AI’s recommendation set thanks to the endorsements and information others have provided. With strong structured data and authoritative citations in place, the final piece is to bring it all together in an actionable plan – a blueprint for consistently winning on the AI shelf.
Blueprint for the AI Shelf Advantage
To secure prime real estate on the AI shelf – that coveted spot where an AI assistant names your product as the answer – brands need a multifaceted game plan. Below is a step-by-step blueprint combining data optimization, content strategy, and continuous improvement:
1. Audit and AI-Optimize Your Product Data: Begin with a thorough audit of your digital product listings. Ensure every product has complete and correct information in a format AI can use. This includes: enabling crawling (check that OAI-SearchBot and others aren’t blocked), implementing or validating JSON-LD schema on all pages (product name, description, price, availability, reviews, ingredients, etc. all marked up) , and syncing your catalog with major feeds. Upload your latest product feed to Google Merchant Center and Bing Merchant Center (even if you’re not running ads) – this keeps you in the loop for Google’s and Bing’s AI shopping results . Most critically, submit your product feed to OpenAI via their merchant sign-up. Being an early participant in ChatGPT’s shopping program can give you a first-mover advantage. In short, make your data clean, rich, and everywhere: the AI can’t recommend what it doesn’t know, and it will favor what it can understand confidently.
2. Speak the AI’s Language in Product Content: Reframe your product titles and descriptions to align with natural language queries. Many legacy product names are cryptic or branded (e.g., “The Audra Set”), which mean little to an AI . Augment them with descriptive keywords a shopper or an AI might use. For example, naming a product “Luminizing Glow Serum – 7% Niacinamide + Vitamin C” directly injects query-friendly terms (benefit and ingredient) into the title. Craft descriptions in a conversational, answer-oriented tone: instead of just listing features, write as if you’re answering the question “what does this product do and who is it for?” Structure information in easy-to-parse ways – use bullet lists for key benefits, a Q&A section for common questions, and include synonyms for problems/solutions (e.g., “dry skin,” “flaky skin,” “dehydrated skin” all in context). This approach helps the AI match your product to varied user phrasings. Remember, the AI is essentially reading your product page; if that page looks like an answer to a shopper’s question, you’re a step ahead.
3. Leverage Ingredient-Level Transparency and Benefits: Especially for beauty and personal care brands, ingredient transparency is a competitive differentiator on the AI shelf. Consumers often ask AI very specific questions like “Which moisturizer has hyaluronic acid and no fragrance?” or “What’s a shampoo with biotin for thinning hair?”. Ensure that your ingredient lists and related benefits are not only clearly stated on your site, but also formatted for easy parsing. Use the appropriate schema or at least HTML lists for ingredients. Provide context – e.g., “Contains 2% salicylic acid (helps unclog pores and fight acne)” – either on the product page or in a linked resource. Not only does this help the AI connect your product to problem-solution queries, it also builds trust. A large segment of consumers (across generations, but notably 81% of Gen Z) view transparency as a sign of trustworthiness. When an AI sees that your brand openly shares what’s inside your product and why, it “knows” you’re a credible player. And if that AI can cite your ingredient or efficacy info from a respected source (say, referencing a clinical trial or a dermatologists’ statement you’ve made public), your product becomes an authoritative recommendation. Action item: Add an “Ingredients and Usage” accordion or section on each PDP with plain language explanations of each key ingredient. Not only will shoppers appreciate this, AI agents will too.
4. Build Citation-Worthy Content and Relationships: Develop a content and PR strategy specifically aimed at generating authoritative mentions of your brand in the digital ecosystem. This means investing in content marketing that goes beyond product promotion: think educational blog posts, how-to guides, expert interviews, infographics – content that others find worth referencing. Simultaneously, do outreach to get your brand included in high-visibility listicles, comparison articles, and expert roundups. If Byrdie or Allure publishes “Top 10 Serums for Winter Dryness,” you want to be on that list (assuming relevance). If Reddit has a thriving community in your niche, consider hosting an AMA or at least ensure that common user queries about your product are answered (authentically, not spammy) with good information. Keep an eye on which domains are the top “hubs” for your category’s AI citations . As Goodie’s research showed, a mix of forums (community), retail PDPs (with reviews), news/editorial sites, and reference sources tends to dominate AI citations . Your strategy should cover all these fronts – a portfolio approach to AEO. Concretely: pitch to appear on at least one major news site or magazine, one popular community or Q&A site, and ensure your product has a robust presence on retailer sites with reviews (even if it means sampling or encouraging happy customers to leave reviews). This diversified presence makes it far more likely the AI will “see” your brand mentioned when formulating answers.
5. Monitor Your Share of Answer (and Adjust): Just as SEO experts track search rankings, you’ll want to track your “AI ranking.” Regularly test key queries on ChatGPT (with browsing/search on), Bing Chat, Google’s SGE, and Perplexity. Note if and when your brand or product is mentioned . For example, ask: “What are the best [product category] brands right now?” or “Tell me about [Your Brand]’s products.” See if the AI includes you and what sources it cites . If you’re absent, dig into why: Is it citing a competitor’s blog or a review site where you’re not listed? That’s a clue to where you need content or presence . If it mentions you but along with a negative or lukewarm context (perhaps citing a mediocre review), that flags an area to improve (generate more positive coverage or address the underlying product feedback). Some companies are now using specialized tools to monitor AI search visibility – tracking not just if you appear, but in what position, with what sentiment, and which sources are underpinning the answer . Whether via tools or manual checks, establish a cadence (e.g., monthly) to measure your AI shelf share. Treat it like a new analytics KPI. Then iterate: use those insights to guide your content and outreach strategy continuously.
6. Ensure Consistency and Trustworthiness Across Channels: AI models aggregate information across the web, so consistency is key. Align your messaging and claims on your website, social media, and third-party content. If your site says one thing (“100% organic ingredients”) but a retailer listing omits that or a reviewer questions it, the AI may register the discrepancy. This doesn’t mean you control third parties, but strive for clarity and honesty everywhere. Importantly, back up claims with evidence. If you say “dermatologist recommended” or “clinically proven,” have a citation or link to back it. An AI scanning your page might actually see that citation and consider it a positive signal (and if a user asks, the AI could mention the study result as supporting evidence). Building trust also means tending to your product’s ratings and reviews. Users often ask AI, “Is [Product] any good?” The AI will look to reviews and general sentiment. While you can’t directly change users’ words, you can ensure customer support and product quality are high so that the organic chatter about your brand remains positive. And if issues arise, addressing them publicly (e.g., a note from founders on improvements) can turn a potential negative into a trust-building moment. Essentially, strive for a transparent, evidence-backed brand presence that an AI would have no qualms recommending to someone as the “trusted answer.”
7. Act Early and Embrace the AI Ecosystem: Finally, recognize that we are still in the early days of AI commerce. This is the window to secure organic visibility before AI shelf space potentially becomes a pay-to-play arena. As one industry observer noted, “the brands that lean in early will own the AI shelf space before it becomes pay-to-play” . Already, early adopters report significant benefits: higher conversion rates from ChatGPT referrals and free traffic from these trusted recommendations . To capitalize, join beta programs, experiment with AI plugins, and even consider building your own AI chatbot for your brand (to gain first-hand understanding of how queries are handled). Participate in pilot programs like Bing’s Chat Commerce or Google’s SGE experiments. The knowledge and presence you build now will be a moat if/when these platforms introduce sponsored slots or if the algorithms get more crowded with every brand vying for attention. In short: start now. Educate your team, allocate resources, and treat AI visibility as a core marketing objective, not a side project. The learning curve is steep, but the reward – being the go-to recommendation in your category – is a game-changer for market share.
In conclusion, competing for space on the AI shelf requires a fusion of what might once have been siloed tactics: technical data management, content marketing, PR, and old-fashioned customer-centric thinking. It’s about ensuring the AI has zero reasons to pass over your product – technically or contextually – and every reason to see it as the best answer. The brands that master this will not only enjoy greater visibility but also consumer trust, because an AI-endorsed recommendation carries an implicit imprimatur of authority. As one expert aptly summarized, “AI visibility is shelf space — and shelf space is sales.” In this new retail reality, the shelf is digital and personalized, but the age-old principle remains: if you win the shelf, you win the customer. By following the blueprint above, beauty brands (and indeed any brands) can position themselves to win that critical recommendation, and with it, the loyalty of the next generation of AI-assisted shoppers.