From Keyword Search to AI-Led Product Discovery
AI-powered search engines are systems that use large language models to answer detailed, conversational queries with curated recommendations, compressing research, comparison and decision-making into a single guided experience that reshapes how people discover and choose products online. Instead of typing short keywords and scanning links, shoppers now ask questions such as “what’s the best skincare for sensitive skin?” and expect a clear, reasoned answer. This shift is visible inside traditional search through features like Google’s AI Overviews, which sit above paid and organic listings, and outside it as consumers start product research directly in tools like ChatGPT or Perplexity. Rather than browsing dozens of pages, users move through a dialogue that narrows options step by step, turning product discovery optimization into a conversation with an AI, not a race for blue-link clicks.
How LLM-Based Search Results Change Brand Visibility
When product recommendations come from LLM-based search results, the exposure model changes. Users see one synthesized answer plus a small set of citations, not a long results page. According to Yext’s 2025 study, 62% of consumers trust AI to guide their brand decisions, even if they still cross-check some results later. That cross-checking often happens after an AI has already chosen a shortlist, so the critical battle is to be included in the initial response. At the same time, SparkToro’s 2024 research shows that nearly six in ten Google searches end without a click, adding to the “zero-click” pressure. For marketers, traditional keyword lists and rank reports miss this new reality: brand visibility in AI search is about influence over the generated answer, not only about where a link appears on a page.
From Classic SEO to AI-Native Product Discovery Optimization
Google’s recent guidance on its generative AI features states that existing SEO best practices still apply because AI experiences draw on core ranking and quality systems. That means relevant, high-quality content remains the foundation of product discovery optimization, but how that content is used by AI-powered search engines is changing. Brands need a broad footprint: authoritative owned content, credible publisher coverage, detailed product information, reviews and comparisons, plus consistent off-site signals. Early analysis shows that citations and recommendations are not identical—an AI may cite a source but highlight a different product. Marketers therefore need to understand which pieces of content influence the final recommendation, not only which URLs appear. Success depends on building topic authority around real user questions and aligning brand messaging with the prompts that matter across the full purchase funnel.
Emna.ai and the Rise of AI Search Visibility Tools
As brand visibility in AI search becomes a priority, new tools are emerging to measure and improve performance inside LLM answers. Tradedoubler’s Emna.ai connects to major LLMs to run market insights around the prompts people ask at each stage of the journey. It tracks where a brand appears in generative answers, calculates share of voice, and pinpoints which domains, articles and publishers are being cited for both the brand and its competitors. Unlike typical SaaS dashboards, Emna.ai is positioned as a campaign tool: it links insights with content creation and publisher activation to close visibility gaps. In an early skincare campaign, a client moved from outside the top five to number four while AI visibility rose from under 5% to 30% in under two weeks, showing how targeted action can shift LLM-based search results.
Building a Continuous AI Search Strategy
Optimizing for AI-powered search engines is not a one-off project but a continuous loop of analysis, content and refinement. Brands must identify the high-value prompts their audiences use, assess current inclusion and ranking in LLM responses, and then strengthen the content that influences those answers. Tools like Emna.ai support this by revealing which articles and publishers shape recommendations and where competitors are gaining share of voice. The practical playbook includes collaborating with authoritative publishers, improving product-level data, investing in independent reviews and creating content tailored to conversational questions. As AI platforms from Google, Amazon and others make product discovery more guided and compressed, marketers who treat AI search as a new performance channel—tracked, tested and optimized over time—will be better placed to stay visible in the recommendation set.