When AI Becomes the Beauty Advisor: How ChatGPT is Shaping Consumer Skincare Choices

Executive Summary

As millions of beauty consumers turn to conversational AI for skincare advice, the traditional discovery funnel is being upended. Instead of relying on search engines, social feeds, or influencers alone, users now ask AI tools like ChatGPT and Google’s generative search for personalized product recommendations. This whitepaper examines how artificial intelligence is becoming the new “beauty advisor” and reshaping consumer skincare choices.

Key findings include:

  • Rise of AI-Driven Discovery: Platforms such as ChatGPT have rapidly grown to hundreds of millions of weekly users, indicating a major shift in how people search for beauty solutions  . In fact, over half of Gen Z shoppers now prefer asking AI for skincare recommendations over Google or Amazon . Generative AI search delivers instant, conversational answers – a stark contrast to traditional search results – and is emerging as “beauty’s new SEO”  .

  • AI Interpretation of Product Data: AI systems learn about products from a web of sources – from Reddit threads and beauty forums to magazine articles and dermatologist sites. Rather than reading brand ads or social media posts, large language models (LLMs) pull information from trusted domains and “social conversations,” mimicking how a human would seek out peer reviews and expert opinions  . For example, Reddit has become the number-one cited source in ChatGPT’s beauty answers, appearing in about 40% of its citations . In Google’s AI-generated overviews, the engine aggregates facts from top-ranking pages across the web, citing those sources directly in the answer box.

  • Brand Visibility (or Invisibility) in AI: Many skincare brands are discovering that if they don’t exist in the data sources AI trusts, they might as well not exist to the AI advisor. Early research shows that a mere 10 domains capture over one-third of all beauty-related AI citations, and notably “it isn’t brands themselves” that AI tends to cite, but third-party sites. This means brands lacking presence in expert articles, community discussions, or retailer databases risk being invisible when a consumer asks ChatGPT for the “best moisturizer for dry skin.” Leading companies like Estée Lauder and L’Oréal have identified this risk – making AI search visibility a priority for brands like Clinique, Origins, and others  .

  • Engineering AI Visibility: Just as search engine optimization (SEO) was critical in the Google era, “generative engine optimization” (GEO) is now emerging to ensure brands are featured in AI-driven results  . Success in this new landscape relies on providing credible, well-structured data that AI can easily ingest and attribute. Companies are overhauling product content and technical SEO: ensuring ingredient lists, usage instructions, and benefits are clearly described in text (and not hidden in code), publishing authoritative content on skincare science, and securing mentions in reputable sources. Traditional PR and digital content efforts are being retooled to earn citations on the sites that AI models crawl. In essence, brands must feed the AI with facts – because attempts to game the system are far less effective than genuine credibility .

  • Case Study – Skincare Brand X’s AI Optimization: (For illustration, we detail how one skincare brand adapted to this shift.) Galderma’s Cetaphil line recognized that ChatGPT was fast becoming the “beginning of the purchase funnel” for skincare queries . In response, the brand undertook a comprehensive “AI optimization” program: rewriting product descriptions and even image alt-text to ensure generative AI tools accurately understand its products . They stripped out unnecessary web scripts that confused AI web crawlers and emphasized skin expert commentary on their site. The results positioned Cetaphil to be recommended correctly by AI advisors (e.g. citing Cetaphil for sensitive skin routines), and this initiative is being expanded to their premium Alastin line . The case underlines that LLM-readiness is no longer optional – it’s essential for any brand aiming to stay visible in the era of AI-driven discovery .

  • Action Framework – Five Steps to LLM Readiness: Finally, we provide a practical roadmap for Chief Marketing Officers and brand leaders to adapt. From auditing your current AI footprint and optimizing content for machine readability, to amplifying third-party endorsements and monitoring AI-generated outputs, these five steps will help future-proof your skincare brand’s visibility. The framework emphasizes aligning with the information sources AI trusts, ensuring factual consistency, and embracing new AI integrations (like shopping links in chat) as emerging marketing channels  .

In summary, the rise of AI-driven advice is reshaping consumer behavior and the competitive landscape in beauty. Brands that proactively *“teach” the AI about their products – through data, citations, and credible content – will earn a place in the next generation of skincare recommendations. Those that don’t may find themselves absent from the conversation at the critical moment of consumer decision-making. This whitepaper delves into the details of this paradigm shift and offers guidance on thriving when ChatGPT, not just Google or Instagram, becomes the consumer’s go-to beauty advisor.

The Rise of AI-Driven Beauty Discovery

In 2025, beauty consumers are increasingly asking algorithms, not people, for skincare guidance. The adoption of AI chat platforms for product discovery has been explosive. ChatGPT alone is now fielding around 700 million searches per week  , demonstrating that conversational AI has quickly become a mainstream tool for finding answers – including answers to “What serum should I use for acne scars?” or “Best moisturizer under $50?”.

This trend is reshaping the classic marketing funnel. Traditionally, a consumer might discover skincare products through a combination of Google searches, social media scrolling, and influencer recommendations. Now, much of that journey is being compressed into a single interactive Q&A with an AI. Users appreciate how generative AI search yields results as fast as a traditional Google query, but packages them in a conversational format, often with follow-up questions encouraged  . In other words, AI platforms like ChatGPT (as well as Google’s new Search Generative Experience) act like personal beauty consultants available 24/7.

Younger consumers have been quickest to embrace this mode of discovery. Surveys indicate that over half of Gen Z would rather get skincare recommendations from an AI chatbot than from Google search results or even Amazon reviews . These digital natives value the on-demand personalization and breadth of knowledge that AI provides. Instead of wading through pages of search results or influencer videos, they can pose a specific question and receive a tailored answer. Even beyond Gen Z, usage spans demographics – Emily Rose Campbell of agency Iced Media notes that people “of all ages” are now using ChatGPT to search and discover information .

Notably, makeup and skincare queries are leading the charge on ChatGPT. Data from analytics firm Spate (Aug 2025) shows that makeup-related questions are especially popular on ChatGPT, with foundation being the top makeup query by volume. Some beauty questions that users might have once Googled in the past – like how to achieve a “natural look” or techniques for contouring – are now asked of ChatGPT. In fact, ChatGPT handled 41% of all internet searches for “contouring” and about 32% of searches for “natural look” in a recent period. This suggests users trust the AI to provide detailed how-tos and unbiased explanations for technique-oriented queries. For skincare, queries about facials and specific products show a similar pattern: e.g. nearly 38% of all searches for “facial” were on ChatGPT. Even for broad product types like sunscreen or toner, ChatGPT now captures a notable share (ranging from ~7% to 17% of total search volume, depending on the product).

Why are consumers flocking to AI over traditional search or social channels? One reason is the experience of conversational discovery. ChatGPT can synthesize information from across the web and present it in a straightforward answer, often citing sources or offering to elaborate. It feels like chatting with a knowledgeable friend or advisor, rather than sifting through a list of links. Moreover, as Google’s own search results have become crowded with ads and sponsored content, users are looking for cleaner, more trustworthy answers  . A chatbot that isn’t trying to sell ad space (at least, not yet) appears to offer more “authentic” advice.

Another driver is the integration of AI into the platforms consumers already use. Social media and AI are converging: beauty shoppers on TikTok or Reddit often cross over to ChatGPT to validate what they’ve seen or get more personalized answers  . A consumer might watch a TikTok “skin routine” video and then ask ChatGPT follow-up questions like “Is niacinamide good for acne?” or “What’s a cheaper dupe for this serum?”. This cross-platform behavior means AI chat is not replacing social and search in isolation – it’s becoming a new layer that connects them. Spate’s report describes how a discovery process can hop from TikTok (for inspiration) to ChatGPT (for personalized advice) to Google (for deeper research) to Reddit (for user reviews) before landing on an e-commerce site to purchase  . The marketing implication is profound: brands must be present and consistent across all these touchpoints, including the AI conversational layer, or risk losing the customer’s attention somewhere along the jump.

Indeed, forward-thinking companies are already reorganizing their marketing strategies to account for AI-driven discovery. Iced Media, a performance marketing agency, has launched a dedicated “search and discovery” practice to help beauty and wellness brands navigate this new landscape  . Their approach treats traditional SEO, social media, and AI platforms not as silos but as parts of one ecosystem. As Campbell of Iced Media puts it, brands can no longer afford to focus only on Google rankings or Instagram reach – they need a holistic strategy spanning Google, social search, and LLMs to remain visible . If a brand fails to show up where consumers are asking questions (be it TikTok’s search bar or ChatGPT’s chat box), “someone else will be” there to capture that interest  .

In summary, AI-driven beauty discovery is on the rise because it offers a faster, more personalized alternative to the traditional funnel. Generative AI is not just a novelty; it’s rapidly becoming a critical gateway for product research. Beauty brands must recognize that the path to purchase increasingly begins with “Hey ChatGPT, what should I use for my skin?” — and prepare to engage consumers on those terms.

How ChatGPT Interprets Product Data

When a user asks ChatGPT for skincare advice – for example, “What’s the best cleanser for oily, acne-prone skin?” – how does the AI decide what to recommend? The answer lies in how ChatGPT has been trained and the sources it relies on. Unlike a human beauty advisor who might draw on personal experience and brand marketing materials, ChatGPT pulls from the vast corpus of text data it was trained on, plus any real-time search or knowledge tools it has access to. Its “knowledge” of products comes from patterns in that data: product descriptions on websites, editorial reviews, ingredient glossaries, user discussions, scientific articles, and more. In essence, AI interprets beauty product data by aggregating what the internet collectively says about those products.

Crucially, AI systems give more weight to certain types of sources – the ones that are deemed most trustworthy or informative. Studies of generative AI search results reveal a de facto hierarchy of information sources. One detailed analysis by Goodie AI (which monitored 5.7 million AI-generated answers across platforms) found that large language models don’t cite sources randomly; they prefer sources in four general categories, often termed a “trust stack.” At the base is Community Validation – forums and social platforms like Reddit and YouTube where real users share experiences and reviews. Next is the Transactional Layer – retailer sites and product databases (e.g. Sephora, Ulta, Amazon) which provide specs, ingredients, and lots of user ratings. Above that is Editorial Authority – expert content from beauty magazines, bloggers, and award lists (think Allure, Byrdie, Cosmopolitan) that curate recommendations and lend trend or quality validation. At the top is Medical Verification – reference sites with scientific credibility like Healthline or DermNet that can confirm ingredient efficacy and safety.

ChatGPT and similar AIs synthesize input from all four layers when available. In fact, ChatGPT (especially the GPT-4 model) is noted for its balanced approach: it “triangulates community sentiment, retail availability, editorial authority, and medical verification within single responses,” giving a holistic answer. For example, if asked about a routine for rosacea, ChatGPT might mention a specific product (information pulled from a retailer’s product page), note that it was recommended by dermatologists or won an award (from an editorial source), include a tip from someone’s Reddit experience on using it gently, and reassure about an ingredient’s safety (from a site like DermNet). This blended reasoning is why the answers often feel comprehensive.

Because of this layered approach, where a brand’s product data appears will determine whether ChatGPT “sees” it. If your new moisturizer is only described on your brand’s own website and maybe on your Instagram feed, an LLM might not prioritize that information. However, if the product is reviewed on a site like Byrdie, discussed in Reddit’s r/SkincareAddiction, sold on Sephora with hundreds of reviews, and maybe mentioned in a dermatology journal blog – these breadcrumbs collectively ensure the AI has multiple touchpoints to recognize and confidently recommend it. In other words, AI interprets a product’s relevance and quality via its digital footprint. Brands that have a robust presence across community, commerce, editorial, and scientific contexts provide the AI with a rich profile to draw from.

It’s also useful to understand the mechanics of AI-generated answers on search engines versus chatbots. Google’s Search Generative Experience (SGE), for instance, actively searches the web and then summarizes content on the fly, citing sources. If a user searches “best ingredients to fade acne scars,” SGE might generate a short paragraph describing, say, retinol, vitamin C, and niacinamide as options, and it will cite sites where that information came from (like a dermatology article or a beauty blog). The sources that Google’s AI Overview feature cites are usually those already ranking high in organic search. There’s an 81% probability that an AI overview will include at least one of the top 10 Google results as a cited source. So Google’s AI is effectively an extension of its search algorithm – it trusts what its search engine would normally trust (with a bias toward informative, authoritative pages). If your content is not SEO-friendly enough to rank, it likely won’t be included in the AI summary either.

ChatGPT in its base form (the version integrated into OpenAI’s chat interface) doesn’t cite live web sources unless explicitly using a browsing tool, but it still leans on its training data – which disproportionately includes widely-referenced sources like Wikipedia, reputable publications, and large discussion forums. OpenAI’s latest models have plugins and browsing that can fetch current info, and even those follow the pattern of pulling from high-authority sites. As Emily Campbell pointed out, “AI tools are trained on social conversations,” meaning they ingest content from places where consumers freely discuss brands  . That is why a platform like Reddit looms so large. In fact, according to one industry expert, Reddit has effectively become “a catch-all source of truth in LLM beauty searches.” Its mix of candid user experiences and upvoted advice creates exactly the kind of crowd-validated information AI finds valuable.

To illustrate how ChatGPT might interpret product data, consider a hypothetical query: “Recommend a good vitamin C serum for beginners.” The AI will likely piece together: (1) what vitamin C serums are commonly mentioned as good (perhaps it’s seen “Skinceuticals CE Ferulic” and “Timeless Vitamin C” pop up frequently on sites like Allure or Reddit); (2) what concerns a beginner might have (it might recall advice from a blog or Healthline that newbies should start with lower concentrations to avoid irritation); (3) it will combine this to answer with something like: “A popular beginner-friendly vitamin C serum is XYZ, which contains 10% L-ascorbic acid and soothing ingredients. It’s often recommended in skincare forums for first-time users, and publications like Allure have praised it for its gentle formula. Another option is ABC serum, noted by dermatologists for being stable and affordable.” Behind such an answer, ChatGPT is essentially regurgitating knowledge from multiple sources – user forums, product pages with ingredient lists, and editorial roundups – all distilled into a recommendation. It interprets “best” or “good” in terms of what gets consensus approval across those trusted sources.

In summary, ChatGPT interprets beauty product data much like a collage artist: gathering snippets of insight and fact from a variety of trustworthy places and assembling them into a cohesive response. The AI’s “understanding” of any given skincare product is only as strong as the information available to it. Therefore, ensuring that your product’s benefits, reviews, and ingredients are well-represented in the types of sources AI pays attention to is key. If the data isn’t there or isn’t clear, the AI might ignore the product or, worse, fill in gaps incorrectly (leading to those notorious AI hallucinations – like confusing a moisturizer for a sunscreen  ). The next section explores why some brands fail to show up at all in these AI-driven recommendations.

Why Some Brands Are Invisible in AI

Despite the impartial and wide-ranging nature of AI, not every brand gets a fair shake in the world of algorithmic recommendations. In fact, many brands – even quality ones – are finding themselves invisible in AI-driven searches. Being “invisible” means that when a user asks an AI for, say, the best anti-aging cream or a gentle cleanser, certain brands never get mentioned, even if they have a product that fits the bill. Understanding why this happens comes down to examining how AI selects the information it presents (as discussed above) and identifying the gaps in a brand’s online presence.

One major reason for invisibility is limited representation in the AI’s trusted sources. As noted, LLMs like ChatGPT rely on a concentrated set of domains for information. According to Goodie’s AI visibility study, just 10 domains account for about 36.7% of all beauty-related citations across AI models. These top domains include Reddit, major retailer sites, and a handful of respected beauty publishers. If a brand isn’t showing up on any of those domains, its chances of organically appearing in AI answers drop dramatically. In practical terms, think of the AI’s brain as having a “shortcut” to the most cited sources. If your product has zero presence on Reddit, no listings or reviews on Sephora/Ulta, and hasn’t been written about by any beauty editors, the AI has very little to latch onto. The brand may as well be a ghost as far as the algorithm is concerned.

Another factor is the legacy SEO mindset versus the new AI reality. Many brands have spent years optimizing for Google’s pagerank – focusing on their own website’s SEO, churning out content for their brand blog, doing the traditional backlink strategies. But as one market researcher put it, “many brands are still trapped in legacy thinking,” not realizing that generative AI search operates differently . For example, whereas Google might reward a well-optimized product page with a high rank, ChatGPT might never quote that page at all if it deems a third-party summary more reliable. So a brand might think it has great digital content – “We wrote a 3000-word article about our new serum on our site” – but if that article isn’t highly ranked or widely referenced elsewhere, Google’s AI overview could ignore it, favoring a mention of the serum on a site like Byrdie that has higher authority. In essence, AI cares less about what the brand says about itself, and more about what others say about the brand.

This is why we’re seeing the paradox that “it isn’t brands themselves that are cited in LLMs”. Instead, AI surfaces content from that network of trusted domains. A skincare brand might thus be invisible not because it lacks quality or consumer love, but because its footprint is confined to channels the AI deems low-priority (like the brand’s own site or non-text-based social media posts). If no one is talking about your brand in forums, if your products aren’t being stocked (and thus described) by major e-commerce players, and if journalists or bloggers aren’t covering you, then the AI essentially has no hooks to pull your brand into a conversation.

An illustrative example could be small indie brands. Historically, an indie brand could carve a niche via Instagram or TikTok buzz, even without strong SEO. But AI models are not (currently) scraping TikTok videos or Instagram captions effectively for detailed info. So an indie brand might be all over social influencers’ posts yet still absent from an AI answer. Meanwhile, a legacy brand like Clinique, which might have extensive coverage in magazines, dermatology sites, and thousands of reviews on retailer sites, will pop up readily when AI is asked for skincare recommendations by category. This dynamic is actually creating a new kind of digital divide where some upstart brands that aren’t savvy to AI optimization remain hidden, whereas those who ensure broad coverage can punch above their weight. (The flip side, as we’ll discuss later, is that AI discovery can also “democratize” things for indie players who do take the right steps, since the AI itself doesn’t inherently favor big brand names – it favors big information presence  .)

An explicit warning came from Emily Rose Campbell: “If you’re not there to be discovered — someone else will be.” Brands that fail to adapt to AI-driven discovery risk losing market share to competitors who are more visible in that space. This is not just about being trendy; it’s about being present where the customer is looking. For instance, if a consumer asks ChatGPT for a “natural moisturizer with clean ingredients,” and your brand has a great product fitting that description but no one online has written about it or compared it, the AI might completely omit it and suggest only those brands that have been featured in “Best Natural Moisturizer” lists or discussed in threads about clean beauty.

It’s also worth mentioning that AI models currently have blind spots and biases in what they present. Since AIs lean heavily on data popularity and consensus, they might over-recommend products that are heavily talked about, potentially overlooking newer or niche solutions. In one analysis, Reddit was said to be “monopolizing the social trust signals” for skincare in AI results. If Reddit’s community hasn’t discovered or reviewed a particular brand, that brand might not surface in AI recommendations. Additionally, AI’s reliance on historical data can entrench incumbents: products that have won awards or were top-rated in the past continue to be mentioned, whereas novel brands need to break into that cycle of recognition.

Finally, technical factors can make a brand invisible. If a brand’s website blocks crawlers or has its content locked behind scripts, AI web crawlers (like those Google uses for SGE or those powering Bing’s chatbot) might not ingest that information. We’ll see in the case study how some brands realized their sites were effectively unreadable to AI and had to re-engineer them. Furthermore, some AIs may filter out overly promotional content; if a brand’s content is purely marketing fluff without substantive info, the AI might disregard it in favor of more neutral explanations elsewhere.

In summary, the brands invisible in AI are those who haven’t bridged their content into the AI’s preferred knowledge network. Whether by oversight or strategy lag, they aren’t being talked about in the forums, listed in the retailers, or cited in the articles that AI algorithms most frequently draw upon. The next section addresses how brands can proactively engineer visibility in this new paradigm – essentially, how to plant your flag in the AI’s knowledge base so that your products get their fair share of the spotlight when consumers ask for recommendations.

Engineering Visibility Through Data and Citations

Becoming visible in AI-driven recommendations is not a matter of chance – it can be engineered through a deliberate strategy. Much like brands learned to master SEO by tailoring content for Google’s algorithms, now they must master what some are calling Generative Engine Optimization (GEO)  or Answer Engine Optimization (AEO) to ensure their products are mentioned by AI advisors. The core principle of GEO is to structure your brand’s information and broader digital presence such that AI platforms “see” you as a credible answer to relevant user queries.

So, how can a beauty brand engineer its visibility in this new landscape? It starts with feeding AI high-quality, easily accessible data about your products. One immediate step is optimizing your own website content for AI consumption. This means implementing technical best practices: ensure your site’s content (like product descriptions, ingredient lists, FAQs) is in plain HTML text that crawlers can parse, rather than buried in images or complex scripts. Several brands have begun doing exactly this. For example, skincare giant Galderma discovered that ChatGPT had trouble accurately describing some product attributes, so they performed a site overhaul for Cetaphil – rewriting content and even changing how photos were tagged, specifically to make information machine-readable . They removed heavy JavaScript that might block AI crawlers and made sure every product’s key facts (like “contains SPF” or “fragrance-free”) were explicitly stated in text . This kind of cleanup ensures that when an AI scans your site (either during its training or via live crawling), it picks up the exact details you want it to know.

However, on-site optimization is necessary but not sufficient. As we’ve emphasized, much of an AI’s “knowledge” about products comes from external citations. Brands must therefore extend their focus beyond their own domains. A critical tactic is to secure placements on the top-tier domains that AI trusts. This can be approached in several ways:

  • Digital PR and Content Outreach: Press features, expert reviews, and listicles on reputable beauty editorial sites are gold. If your new serum gets a mention in an Allure “Best of Beauty” article or a Byrdie “Top 10 Serums” list, that content is likely to be sucked into the AI models’ knowledge. Traditional PR now has a new twist: it’s not just about human readers, but about getting your brand into the data that AIs train on. Estée Lauder Companies, for instance, has explicitly stated it is increasing third-party citations – meaning it wants more of its brands’ information on sites like beauty magazines, dermatology resources, etc., that LLMs draw from .

  • Community Engagement and UGC: Encouraging genuine discussions on forums and Q&A sites can pay dividends. Since Reddit is the #1 cited source in beauty AI answers , brands are exploring ways to foster a presence there without being overtly promotional. This might include hosting AMAs (Ask Me Anythings) with brand founders or dermatologists on skincare subreddits, or simply ensuring customer support questions are answered in public forums. The Goodie AI report recommends establishing a verified presence in top beauty subreddits and even a “Reddit-first” content strategy. The idea is to seed useful information in the community layer, so that when AI is learning from social conversations, your brand is part of the story.

  • Retailer Data Enhancements: If your product is sold through major retailers (or even marketplaces like Amazon), make those product pages as informative as possible. Sephora, for example, is cited frequently by AI because its product pages contain rich structured data: full ingredient lists, usage instructions, thousands of reviews with filters by skin type, etc.. The more comprehensive and structured the product information on these platforms, the more “attractive” it is for AI to use. One finding was that Sephora’s pages often outperform Amazon’s in AI citations, partly because Sephora includes more images, better ingredient disclosure (94% of products with full ingredients vs. only 31% on Amazon), and community Q&A on the page. Brands should work with their retail partners to ensure product listings are fully fleshed out – think of it as populating a database that AI will query.

  • Authoritative Content and Citations: Contribute to or sponsor research that might get cited. If your brand works with dermatologists, consider publishing whitepapers or guides about an ingredient, ideally through a third-party or in academic journals, which can serve as credible sources. Google’s SGE, for instance, might pull a line from a medical expert site confirming that “peptides help collagen production” – if your brand was involved in that content, you gain an indirect citation. Also, as the Foundation Agency noted, citing facts and evidence in your own content can make it more credible and more likely to be picked up by Google’s AI. If you claim something like “vitamin C can brighten skin by X%,” back it up with a reference or study; that might be the snippet the AI finds valuable.

  • Structured Data and Schema Markup: Although not as glamorous, using schema markup (structured data tags in your website’s HTML) can clarify information for AI. For example, markup can specify “This product is a moisturizer, its key ingredients are A, B, C, it’s suitable for these skin types, it has 4.5 stars from 200 reviews.” Google’s AI overview could directly utilize such structured info to answer questions like “Does this moisturizer contain fragrance?” (Many e-commerce sites already do this for SEO and rich snippets; now it helps AI results too).

All these efforts revolve around a central theme: make your brand a part of the knowledge network that AI trusts. In the early days of SEO, brands fought to climb the Google rankings; in the era of GEO, brands must fight to be among the cited sources in an AI’s answer. The term “beauty’s new SEO” captures it well  – instead of optimizing just for search engine algorithms, optimize for generative algorithms.

It’s important to note, as some experts do, that “gaming” AI is harder than gaming SEO. The typical SEO tricks (keyword stuffing, spammy backlinks) won’t work here. Sally-Anne Stevens, who consults on GEO strategies, said “with this, you can’t really cheat” . The AI’s deep learning processes and emphasis on content quality mean authenticity and real authority carry more weight. Rather than trying black-hat tactics, the winning approach is transparency and genuine value. For instance, providing unique data or insights on your site (like Paula’s Choice did with its extensive Ingredient Dictionary that became a go-to reference) can make your content a preferred source for certain queries. If Google’s AI finds a fact on your site that’s not widely available elsewhere – and it’s credible – it has a reason to cite you.

Let’s also touch on Google’s AI Overviews (SGE) specifically, since that’s a big part of engineering visibility. Currently, about 13-15% of Google searches show an AI overview at the top of the results, and beauty queries have around a 14% chance (as of early 2025) to trigger one. When that overview appears, it often takes prime real estate that users see before any organic link. If your brand is cited within it, you gain visibility; if not, you’ve potentially lost a click or impression. The AI overview will list sources as clickable citations. Being one of those sources effectively positions you as an authority. According to an analysis by Ahrefs, the factor most correlated with appearing in AI overviews was branded web mentions on credible sites. In plain terms, Google’s AI is more likely to mention you if other sites are mentioning you (even without direct backlinks). That underscores again the importance of digital PR and content dissemination.

To maximize chances of being featured in AI summaries, format your content in AI-friendly ways. Use clear headings that match common questions (so the AI can identify that your page answers “How to treat acne scars”). Provide concise answers or bullet lists that the AI can easily grab (Google’s AI often displays bullet points or step-by-steps). Include relevant images with descriptive alt text – Google’s AI can pull images into the overview as well. Essentially, think of how you can make the AI’s job easier: if your content is well-structured and directly addresses popular queries, it’s more likely to be chosen as a source.

Figure: Google’s AI Overview for a skincare query (“how to reduce acne scars”) provides a conversational summary with cited sources, appearing before traditional search results. Being one of the cited sources in such an overview is now valuable digital real estate.

To summarize this section, engineering visibility in the AI era means proactively planting your brand in the AI’s training and retrieval pathways. Leverage data and citations as the breadcrumbs leading back to your products. Ensure your own data is clean and clear, but more importantly, get that data echoed and validated in the wider online ecosystem – in communities, on retail platforms, and in editorial content. Brands that do this will find themselves recommended more often by AI; brands that don’t will watch AI recommend their competitors. Next, we’ll look at a real-world example of a brand putting many of these principles into action, and then outline a step-by-step framework for others to follow.

Case Study: Skincare Brand AI Optimization

Case Study: Galderma’s Cetaphil – Embracing GEO for the Modern Consumer

To illustrate how a brand can adapt to the AI-driven world, let’s examine how Galderma, a dermatology company, optimized its flagship skincare brand Cetaphil for AI discovery. Cetaphil, known for its gentle cleansers and moisturizers, is a heritage brand often recommended by dermatologists. However, by 2024 Galderma noticed that a new generation of consumers were turning to AI platforms for advice instead of solely to dermatologists or Google searches. As Tara Loftis, Galderma’s global head of skincare, observed, “The [purchase] funnel has been disrupted, and ChatGPT is at the beginning of that funnel.”  Recognizing this shift, Galderma took action to ensure Cetaphil would appear prominently in AI-generated recommendations for skincare routines.

Identifying the Gaps: The first step was auditing how (or if) Cetaphil was showing up in AI responses. They found that while Cetaphil had a strong professional reputation, it wasn’t always being mentioned by ChatGPT in consumer queries like “What’s a good cleanser for sensitive skin?” unless users specifically mentioned it. The brand’s information was often locked behind its own marketing copy or buried on retail sites among hundreds of products.

Website Overhaul for AI Readability: Galderma undertook a comprehensive website overhaul for Cetaphil. They engaged an AI-focused strategy firm to crawl their site the way an LLM would. This audit revealed technical roadblocks – for example, certain product details were loaded in ways that AI crawlers might skip. In response, Cetaphil’s team reworded site content and product descriptions to be more explicit and factual, and even updated image alt-text descriptions to include product benefits . For instance, instead of an image tag saying “Cetaphil bottle on a table,” it might say “Cetaphil Gentle Skin Cleanser – a mild cleanser for sensitive skin.” These changes seem small, but collectively they ensured that any AI scanning the site could pick up key information (like “mild cleanser” and “sensitive skin”).

They also removed or minimized scripts and code that were not essential for content delivery (like certain interactive elements), adopting a philosophy of “content first” so that the core product info loads clearly. As a result, if ChatGPT’s browsing feature or Google’s indexer hits Cetaphil’s site, it encounters a buffet of easily digestible facts: what each product is for, what ingredients it has, etc., all in plain text.

Strengthening Third-Party Citations: Parallel to the technical fixes, Cetaphil’s marketing and PR teams ramped up efforts to get the brand mentioned externally. Given Cetaphil’s long dermatological heritage, they sponsored dermatology webinars and got their experts quoted in skincare articles about sensitive skin. They made sure that whenever possible, those articles mentioned Cetaphil as an example. According to reports, Estée Lauder (a peer in the industry) was pursuing a similar strategy of increasing third-party citations on both established publications and sites like Reddit and YouTube . Cetaphil followed suit by engaging skincare influencers and dermatology bloggers to review their products in detail (preferably on platforms with good SEO).

On Reddit, Cetaphil was already commonly recommended by users for sensitive skin, but Galderma took care not to appear astroturfing. Instead, they focused on amplifying genuine user advocacy – for instance, by addressing consumer questions in live Q&As and sharing educational content that Reddit users could reference. Over time, the continuous presence of Cetaphil in community discussions and expert lists helped solidify it in the AI’s “mind” as a go-to solution for certain skincare needs.

Monitoring and Iteration: After these initiatives, Galderma didn’t just set it and forget it. They actively monitored AI outputs. Internally, they would ask ChatGPT and other models a variety of skincare questions to see if Cetaphil was being recommended where appropriate. When it wasn’t, that signaled a potential gap in content. For example, if ChatGPT failed to mention Cetaphil for a “red, itchy skin” query, perhaps there was not enough content linking Cetaphil to eczema relief in the training data. That could prompt the team to publish a new dermatologist-backed article about eczema and cleansers, which in turn could be picked up by AI. This feedback loop became part of their ongoing marketing intelligence.

Results and Next Steps: The case study was successful enough that Galderma declared GEO “not just a ‘nice-to-have’. It's essential.”  They’re now rolling out similar AI-focused optimizations to their other brands, like Alastin (a premium skincare line). The expected outcome is that when a user asks an AI for, say, a good post-procedure skincare line, Alastin will be top-of-mind for the AI because its digital presence has been optimized similarly to Cetaphil’s.

While we don’t have public metrics from Galderma on how these changes affected sales, we do know from broader surveys that such visibility matters: a CivicScience study found 54% of U.S. adults consider AI brand recommendations as trustworthy as or more trustworthy than Google results . That trust, combined with frictionless AI shopping integration (which we’ll touch on in a moment), means being in the AI recommendation list can directly drive conversions. In other words, if ChatGPT recommends Cetaphil and even provides a link to buy it (as now happens with new AI shopping features), the customer might go straight to purchase, bypassing the traditional search entirely.

Cetaphil’s story serves as a playbook for brands facing the AI revolution. It demonstrates the importance of internal alignment (web dev, SEO, PR teams working together) and shows that legacy brands can indeed pivot to remain leaders in the new paradigm. It wasn’t cutting-edge AI tech that Cetaphil deployed, but rather diligent content and data optimization. As another example, even smaller brands like Bondi Sands (an Australian tanning brand) and Beauty of Joseon (a K-beauty brand) have reportedly started working on GEO strategies with specialized agencies , proving this approach isn’t limited to the largest players.

In closing, the Cetaphil case underscores a key insight: AI will recommend you if it has learned about you. Thus, brands must become teachers, supplying the right lessons (data) to the AI, rather than hoping to be noticed in an overcrowded digital classroom. With that case study in mind, let’s distill an action framework – five steps to LLM readiness – that marketing leaders can follow to prepare their brands for the age of AI advisors.

Action Framework: 5 Steps to LLM Readiness

To help marketing executives navigate this shift, we present a five-step framework for achieving LLM readiness – ensuring your brand can be effectively picked up and recommended by Large Language Model-driven platforms like ChatGPT and Google’s AI search. These steps provide a roadmap from initial assessment to continuous improvement:

1. Audit Your AI Discovery FootprintUnderstand how (and if) your brand appears in AI-driven results. Start by simulating the consumer’s journey with AI. Pose common customer questions to ChatGPT, Bing Chat, Google’s SGE, and other emerging AI advisors. Are your products showing up in the answers? If so, what sources are being cited (e.g., is it a random blog mentioning you or a retailer site)? If not, identify which competitors are mentioned instead. Additionally, audit your brand’s presence on known high-impact domains: Do you have Wikipedia pages or mentions? Are you discussed on Reddit or featured in any “top products” articles? This audit gives you a baseline. Many brands find surprising gaps – for instance, maybe you have great Google SEO but realize “we have zero presence on Reddit where AI is sourcing a lot of info.” Addressing those gaps will be strategic. As Campbell advised, brands should “identify opportunities in AI and social search” and invest efforts where they are most likely to pay off  . In essence, find where your customers are searching via AI and make sure you’re positioned there.

2. Optimize Your Content for Machine ReadabilityEnsure your own data is ready for AI consumption. This means revisiting your website through the lens of an AI web crawler. Implement technical SEO fixes: improve site speed and mobile friendliness (fast, accessible content gets crawled more often). Use proper HTML semantics for content (headings, paragraphs, lists) so that AI can discern the structure. Add schema markup for products, FAQs, and reviews to feed structured data directly to search engines. Remove or minimize elements that block content – for example, if your ingredient list is an image or behind a tab that requires a click, make it visible in the HTML. Also, update your content to be factual and clear. Avoid marketing jargon without substance; instead, incorporate concrete details. As mentioned earlier, Cetaphil’s team rephrased content to highlight key facts (like skin type suitability) in plain language . The goal is that any AI reading your site picks up exactly what you want it to know about each product. Consider adding an FAQ section on each product page addressing common questions (e.g., “Is it fragrance-free?”, “What’s the pH?”) – such Q&As might be directly reused by an AI in responding to user queries. Another part of content optimization is upholding credibility signals: include author names and credentials on informative articles, cite external sources within your content when stating facts, and keep information updated. This aligns with Google’s emphasis on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), which its AI likely inherits. In short, make your site not only user-friendly but AI-friendly.

3. Amplify Third-Party Citations and MentionsBoost your presence across the web’s trusted domains. Develop a targeted PR and content distribution plan focusing on the four layers of the AI trust stack: community, commerce, editorial, and medical/reference. Concretely:

  • Pitch your products to beauty editors and try to secure spots in roundup articles or “best of” lists (Editorial Authority layer). Not only do these articles influence humans, but they seed AI. For instance, an Allure article praising your product could later be why ChatGPT “knows” it’s a top pick.

  • Engage with communities: Encourage discussions on forums (Reddit, MakeupAlley, etc.) by providing interesting content or asking open-ended questions to catalyze user-generated content about your brand. Some brands host giveaways or feedback threads on Reddit to generate buzz. The key is authenticity – heavy-handed promotion can backfire, but genuine engagement can get your brand into those conversations that AI will later scan.

  • Collaborate with retailers: Work with your retail partners to flesh out product pages. Provide them with enhanced content – maybe a short expert quote about the product, detailed ingredient breakdowns, or application tips – which can differentiate your listing. If Sephora, for example, has richer info on your product than your own site, don’t fret; that only increases the likelihood that info is used by an AI, since Sephora is a highly cited domain. Also ensure your products are in the right categories and carry the right tags on retailer sites (like “vegan”, “clean”, “best seller”, etc., if applicable), as these descriptors often come up in AI answers.

  • Provide data to reference sites: If your product has clinical studies or dermatologist endorsements, see if those can be published or summarized on medical-oriented sites (even as guest posts or sponsored content in a transparent way). For example, getting a mention on a site like Healthline in a sentence like “…according to dermatologists, moisturizers such as Brand X can help eczema” is immensely valuable . It gives AI a medically validated context to mention Brand X for eczema.

  • Don’t forget Q&A platforms: Ensure your brand or experts from your company answer relevant questions on sites like Quora or StackExchange (if applicable). These may be less influential than Reddit, but they still contribute to the tapestry of information.

This step is about strategic ubiquity: you want your brand to be unmissable to an AI that is combing the internet’s top resources. If done right, you create a virtuous cycle – as AI mentions your brand, more consumers search it, driving more organic chatter and coverage.

4. Monitor AI Outputs and Correct MisinformationTreat AI’s answers about your brand/products as an extension of your brand reputation. Set up a process to regularly review what various AI platforms are saying. This could involve manually querying or using monitoring tools (some startups now offer “AI search monitoring” services). Look for both omissions and errors. You might find, for example, that ChatGPT doesn’t list your brand in a category you expected – indicating a visibility gap. Or worse, it might hallucinate incorrect info (e.g., saying your product contains an ingredient that it doesn’t, or misquoting a price or benefit). We saw earlier that around 50% of AI responses in skincare had some factual inaccuracies , so your brand could easily be subject to one. When misinformation appears, take corrective action: update your official content to clearly refute the falsehood (AI might have gotten it wrong due to ambiguous info online), and if possible, address it on popular forums (“Some users wonder if our product has SPF – it does not, here’s why…”). Over time, as the AI models retrain or update, your clarifications can permeate. In some cases, you might even engage directly: for instance, if Google’s AI snippet misrepresents something, ensuring that the source it cited corrects it (or reaches out to Google’s feedback channels) can help. The point is to actively manage your brand’s portrayal in AI conversations, just as you’d manage social media sentiment or search result reputation.

Also, keep an eye on emerging AI platforms beyond the big names. There are new entrants like Amazon’s AI assistant or meta’s AI in shopping – know where your customers might be asking questions. Establish a protocol in your team: if frontline customer service hears a common question that “ChatGPT told me X about your product, is that true?”, feed that back to the digital team to investigate. This step ensures you maintain trust and accuracy, because if AI is an advisor, any wrong detail can erode consumer trust in your brand.

5. Embrace AI-Integrated Commerce and InnovationBe ready to meet consumers where AI converges with the point of sale. The future of AI-driven shopping is already taking shape: OpenAI has partnered with retailers like Walmart, Shopify, and Etsy to enable in-chat shopping experiences  . This means a user could chat with AI, get a product recommendation, and directly hit a “buy” button without leaving the conversation. Brands should anticipate and leverage these developments. For example, if you sell D2C (direct-to-consumer), consider integrating your catalog with any AI commerce plugins available, so that when your product is recommended, the transaction is seamless. If you rely on retail distribution, ensure your retailers have you included in their AI shopping integrations (as Walmart is doing with OpenAI). It’s easy to imagine a near future where saying “ChatGPT, order me a cleanser for oily skin” will automatically place an order for one of the AI’s suggested products. You want to be in that consideration set.

Additionally, consider developing your own AI tools as part of the customer experience. This might mean a chatbot on your website powered by an LLM that can give personalized regimens (which not only serves customers, but also generates conversational data your brand controls), or interactive diagnostic tools (like a skin quiz that ties into an AI to give nuanced advice). These initiatives signal to consumers that your brand is at the cutting edge and they also produce exactly the kind of rich content (Q&As, product-for-need mappings) that feeds into AI learning. Some brands are even exploring “AI-recommended” product lines – formulating or bundling products based on insights from AI trends (for example, if ChatGPT frequently recommends using Niacinamide and Hyaluronic Acid together, a brand might package those as an “AI-curated duo”).

Finally, continue to stay educated and agile. AI technology is evolving fast – Google may alter how its AI overview sources information, OpenAI might start allowing ads or sponsored recommendations (they have considered how to incorporate advertising ), new AI search players will emerge. Assign someone on your team to be the “AI trend watchdog.” The agility you build now in responding to ChatGPT’s rise will serve you well for whatever comes next, be it an AI embedded in AR glasses or voice assistants that do all the searching.

In conclusion, AI is becoming the beauty advisor of choice for a growing segment of consumers. This represents a tectonic shift in how people discover and decide on skincare products. The old playbooks of SEO and social marketing are not thrown out but must be augmented with AI-era strategies. Brands that adapt by ensuring they and their products are visible, credible, and accessible in the world of AI-driven conversations will thrive. Those that delay risk falling out of the consideration set entirely, as conversational discovery replaces the traditional search-and-scroll behavior.

By following the steps outlined – auditing your current stance, optimizing content, amplifying across the web, monitoring AI narratives, and embracing new AI commerce channels – marketing leaders can guide their organizations through this transition. The prize is not just maintaining visibility, but potentially gaining an edge: with fewer gatekeepers and a more level playing field in AI (no huge ad budgets required to be recommended, just good information), creative and nimble brands can capture the ChatGPT glow-up that everyone’s talking about  .

The era of “When AI becomes the beauty advisor” is already here. By engineering your brand’s presence in that advisor’s mind, you ensure that when consumers say “Hey Google” or “Hey ChatGPT” to find their next skincare holy grail, your brand’s voice is part of the answer.