Introduction: AI Searches & LLMO for Online Brands

Digital-native brands know the importance of Google search and social media for driving traffic. But there’s a new player in town shaking up how customers discover products: AI-driven search and recommendation. Shoppers are increasingly turning to AI chatbots as personal shopping assistants. They might ask, “What’s the best skincare set for under $50?” and get a conversational answer listing a couple of products. If you’re a direct-to-consumer (D2C) brand, this is huge. Consumers love convenience, and AI provides instant, curated suggestions. It’s no surprise that more than two-thirds of Americans (69%) say they’ve interacted with AI-generated results when searching for products online. Generative AI is essentially becoming the new “window shopping” – except it’s happening through a chat interface.

Enter LLMO (Large Language Model Optimization) – essentially SEO for AI. LLMO means structuring your product content and digital presence so that AI chatbots (like ChatGPT, Bing Chat, or emerging shopping assistants) recognize your brand and recommend your products when users ask for advice. Think of an AI like an uber-smart shop assistant who has “read” everything about all products. If a customer asks it for the best hiking backpack, the AI will pull from what it “knows” – product descriptions, reviews, blog posts, maybe even forum comments – to suggest a few options. Your goal with LLMO is to ensure your backpack is among those suggestions. In this post, we’ll discuss why optimizing for AI discoverability is critical for e-commerce and D2C brands, backed by the latest stats, and how you can do it in a human-friendly, non-technical way.

Relevance: Why AI Discoverability Matters for E-Commerce

Today’s consumers are savvier and more demanding than ever – they want quick answers, personalized recommendations, and honest comparisons. Generative AI is perfectly suited to deliver that. Recent research highlights just how relevant this has become for online shopping. According to Adobe’s survey of thousands of U.S. consumers, 39% have already used generative AI for online shopping, and 53% plan to do so in 2025. In other words, over half of online shoppers will soon be using AI tools to help them find or decide on purchases. Even more striking: over 60% of consumers now use AI chatbots for product research before making buying decisions. Instead of reading a dozen reviews or blogs, people are asking an AI, “What should I buy?” and trusting it to do the legwork.

For D2C brands, which often rely on building a direct relationship with customers, this is a wake-up call. If a potential customer asks ChatGPT or Bard, “What’s a good affordable watch from an independent brand?” and your product doesn’t come up, you’ve essentially lost that customer without even knowing they were looking. Product discoverability through AI can directly translate to traffic and sales – when ChatGPT recommends three project management tools or three skincare lines, those brands often see spikes in interest, while competitors left out might as well not exist in that conversation. In an Evercore survey, users even rated ChatGPT’s usefulness for researching products slightly higher than Google’s, indicating they were happier with AI’s shopping advice by a small but notable margin‍. Clearly, people find value in how AI presents product options.

Already, we can see AI-driven referrals translating to real web traffic. From mid-2024 to early 2025, web traffic coming from AI “assistant” referrals (like ChatGPT links) jumped over tenfold in the U.S.. Adobe reports that AI-referred visitors on retail sites are not only growing in number but they’re highly engaged – these visitors often convert as well or better than regular search visitors because the AI has pre-qualified them with tailored recommendations. In sectors like retail, travel, and banking, AI referral traffic has been doubling every 2–3 months. Just look at the trajectory: traffic from generative AI tools to retail websites grew exponentially in late 2024, with retail seeing over a 1000% increase in AI-driven visit share in a matter of months. This explosive growth shows no signs of slowing.

All of this underscores a simple truth: if you’re an e-commerce or D2C brand, optimizing for AI isn’t optional – it’s becoming essential. It’s where the customers are headed. Those brands that get in front of this trend will capture shoppers who prefer asking an AI over scrolling through endless search results. Those that don’t may find their organic traffic and even paid ad effectiveness starting to slip as more eyeballs shift to AI-curated answers and product recommendations. Being part of the AI conversation means your brand becomes one of the handful that gets suggested, which can dramatically shorten a customer’s path to your checkout page.

Future Outlook: How AI Will Shape Product Search & Shopping

The near future promises an even deeper fusion of AI with the shopping journey. Already, 36% of AI users say they’ve replaced traditional search engines with AI assistants for at least some of their search needs‍, and 25% are using AI specifically for shopping and price comparisons‍. As generative AI becomes more accessible (built into our phones, search engines, and voice devices), we can expect a majority of online shoppers to rely on it for product discovery. Picture a scenario where a customer can simply say to their smart glasses or car dashboard, “I need a birthday gift for my 10-year-old niece,” and the AI instantly suggests a couple of ideal products with reasons (“This STEM toy is very popular and within your budget”). That scenario is approaching quickly.

Big tech companies are heavily investing to make this the norm. Google is integrating AI into shopping searches – for example, it’s testing AI-generated “shopping guides” that summarize the top products and reviews when you search for things like electronics or appliances‍. Amazon, not to be left behind, is reportedly working on more advanced AI recommendation features in its marketplace. Startups like Perplexity and others are rolling out AI search engines that specialize in giving buying advice. Meanwhile, the social side of shopping could get an AI twist too: envision AI influencers that give personalized product tips on platforms, or community-driven Q&A where AI aggregates opinions of thousands of users for you. The lines between search, reviews, and personal shopping assistant will blur.

For D2C brands, one especially interesting development is personalized AI agents. We might soon have AI that knows your preferences (from past purchases, browsing history, etc.) and it will proactively suggest products it knows you’ll love, almost like a concierge. This could mean a higher likelihood of discovery for niche brands that perfectly match a user’s taste – if the AI has sufficient info about them. On the other hand, it also means competition could be fiercer for that single recommendation slot. Instead of ten blue links on a page, it might be one or two spoken suggestions from Alexa or Google Assistant. This “winner takes all” dynamic is why Gartner’s prediction of 50% of search traffic going away by 2028 rings alarm bells – much of that traffic will have been consolidated into a few AI-driven outcomes.

The future also holds new opportunities: AI might help small brands punch above their weight by focusing on merit and specifics. For instance, if your eco-friendly sneaker truly has better reviews or materials than a big brand’s, an unbiased AI could recognize that and recommend yours first to a sustainability-minded shopper. AI, in theory, will surface the best fit for the query, not just the biggest ad spender. In Adobe’s research, by early 2025 over 90% of consumers across generations said AI improved their shopping experience (finding better products, easier decisions). That means shoppers trust AI’s picks – and that trust can transfer to the brands AI suggests. The takeaway: the next few years will likely see AI become a primary gateway to e-commerce. Brands that adapt will ride the wave (with potentially lower customer acquisition costs, if you can get organic AI recommendations), while brands that ignore it risk missing out on a growing segment of consumers who simply won’t find them through traditional means.

What Should E-Commerce Brands Do Now to Optimize for AI Searches?

Getting your products on an AI’s radar might sound complex, but it mostly involves doubling down on authentic, high-quality content and data that you may already be working on. Here are concrete steps for LLMO in the e-commerce context:

• Enrich Your Product Data: AI thrives on data. Ensure your product descriptions are detailed and informative – include specs, materials, use-cases, and comparisons. Don’t just say “high quality material,” specify it (“100% organic cotton, durable for 50+ washes”). Such specifics not only appeal to customers but also give AI more to latch onto when determining what sets your product apart. Use structured data (schema markup like Product, Review, FAQ) on your product pages; this makes key info easily digestible for AI. If an assistant is compiling a list of, say, “waterproof fitness trackers,” it will scan for those attributes. If your listings clearly state “waterproof up to 50m” in structured data, you’re more likely to be included in that AI-curated shortlist.
  
  
• Leverage Customer Reviews and UGC: Shoppers trust other shoppers – and so do AIs. Reviews and user-generated content (UGC) often contain the natural language that people use to praise or critique products. That’s exactly the language an AI will mirror when recommending. Encourage customers to leave reviews, especially describing what the product solved for them (“This humidifier helped my allergies within days!”). If you’re a D2C brand, you might already collect reviews on your site; consider also having a presence on third-party review sites relevant to your niche (for example, tech products on TechRadar or CNET, beauty products on MakeupAlley or Sephora). AI models have been trained on large swaths of the internet, which likely include those review platforms. A statistic to note: 85% of shoppers trust reviews as much as personal recommendations (and an AI essentially amplifies those reviews in its answers). By having plentiful positive reviews, you not only influence human shoppers but also feed the AI positive signals about your brand’s quality.
  
  
• Create Comparison and Advice Content: One way to “get into the mind” of AI is to create the kind of content it might draw on. Write blog posts or guides that compare your product with others (fairly and honestly). For instance, “How Our XYZ Vacuum Compares to Leading Brands” or a guide like “Top 10 Gifts for Gamers (Featuring [Your Product])”. If these articles live on your site (and you promote them to rank or be shared), an AI could ingest that information. Even better, contribute guest posts or get featured in editorial articles on reputable sites – e.g. a tech blogger includes your gadget in a “Best of” list. When a user asks an AI “which gadget should I buy?”, the AI may recall that “TechRadar listed [Your Gadget] as the best value”‍. The more authoritative third-party content highlighting your product, the higher the chance an AI will confidently recommend it. Essentially, treat AI like a super well-read customer: if your product is mentioned and praised in many publications and forums, the AI “memory” will reflect that consensus.
  
  
• Engage in Q&A and Community Discussions: People often ask for product advice in forums (Reddit, StackExchange, Facebook groups). These conversations can later inform AI answers since models are trained on public data. Without being spammy, participate where it makes sense. If someone asks “What’s a good beginner yoga mat?” on a forum and your brand makes one, you or a fan of your product could provide a helpful answer mentioning it. Similarly, use your brand’s social media or YouTube to answer common questions (“How to choose the right size backpack?”). Many AIs are already integrated with these platforms or trained on their content. For example, a well-liked answer on Reddit that mentions your product might later surface in an AI’s training data. We’ve seen generative AI output cite Reddit opinions and Quora answers in some cases, meaning those voices carry weight. Being part of these conversations increases the likelihood your brand gets a nod in AI-generated advice down the line.
  
  
• Monitor and Adapt with AI in Mind: Start treating AI outputs like a new search engine to optimize for. Periodically, try queries relevant to your niche on AI chatbots: “best organic coffee beans”, “affordable alternatives to [competitor]”, etc. See if and how your brand is mentioned. If not, identify why. Maybe the AI pulled info from a Wikipedia article or a top-10 list you’re not on. That gives you a clue: perhaps creating a Wikipedia page for your brand (if notable enough) or getting onto more comparison lists could help. If the AI provides an answer that includes your product but with outdated info, that’s a sign to ensure your published data is up to date everywhere. Because AI training data can lag, make sure any critical updates (new pricing, new product lines) are well-publicized on major outlets – consider press releases or newswire announcements for big changes, as those might be ingested by models or at least available for tools like Bing to fetch in real-time. Additionally, consider new tools that emerge for LLMO auditing. Nukipa Brokr, for instance, is built to help brands audit their AI visibility. It can simulate AI queries and show you if your product appears, then provide suggestions (maybe adding certain keywords or content to boost relevance). Utilizing such a tool can give you a head start in the AI optimization game, just like early SEO tools helped brands dominate Google searches.
  
  
• Double Down on Brand Identity & Story: This is a bit less tactical, but important. AI recommendations often come with a rationale. You want that rationale to play to your strengths. Maybe it’s “brand X is known for its sustainability” or “users love that brand Y is family-owned and responsive to customers.” These narratives come from your brand story permeating the web. So make sure you tell it! Share your mission (on your site, social profiles, PR articles), highlight your unique selling propositions loud and clear. When an AI summarizes you, those key points should shine through. For example, if a shopper asks an AI, “What’s a good ethical fashion brand?”, and your company has made that a core part of its messaging (with blogs about your fair-trade factories, news articles about your give-back program), the AI is likely to pick up on that and include you as a suggestion, saying something like “You might like [Brand], they’re praised for their ethical sourcing and quality.”
  
  

In short, winning at LLMO for e-commerce is about feeding the AI quality ingredients: accurate data, genuine praise from customers, and rich content about your products. This isn’t about tricking a system; it’s about genuinely being the kind of product that deserves a recommendation and making sure that information is out there. It aligns with a fundamental of Shopify-styled advice: provide value, tell your story, and meet your customers where they are – and increasingly, they’re with AI.

Conclusion: LLMO – The Next Frontier for Digital Brands

AI-assisted shopping is no longer a futuristic concept; it’s here, influencing millions of purchase decisions in the US and EU alike. As a small or medium e-commerce brand, you have the agility to adapt quickly. By embracing LLMO strategies now, you can carve out a space for your products in the emerging AI recommendation engines. This is akin to early-day SEO or social media marketing – an opportunity to leapfrog bigger competitors if you execute smartly and authentically.

Remember, at the heart of LLMO is understanding your customer. AI is simply becoming the intermediary connecting your product to the customer’s need in a more conversational, immediate way. If you focus on creating the best product and communicating its value effectively across digital channels, you’re halfway there. The other half is tweaking and broadcasting that information so AI can pick it up readily. It might feel new, but as we’ve discussed, many steps (like engaging customers and generating quality content) are things you’re already doing or can start doing without a PhD in computer science.

Don’t be intimidated by the tech. You don’t have to build an AI to benefit from AI. Use the tools and insights available – even simple ones like searching your brand on ChatGPT – to guide your approach. And consider leveraging specialized platforms like Nukipa Brokr that are designed to help brands boost their AI discoverability. They can save you time and point out blind spots in how your products are represented to AI-driven services.

Ultimately, brands that treat AI as an opportunity rather than a threat will be the ones that thrive in this next era of commerce. By optimizing for AI now, you’re planting seeds for long-term growth, ensuring that as more consumers shift to conversational search and smart assistants, your products will be recommended, talked about, and chosen. So go ahead – start implementing LLMO best practices, and let Nukipa Brokr lend a hand in turbocharging your AI visibility. The playing field is still being defined, and with the right moves, your D2C brand can become an AI-era success story.