From Keywords to Conversations: What AI Search Means
AI search optimization is the discipline of improving a brand’s presence in answers generated by large language models, which rank content by intent, context, and usefulness instead of simple keyword matches, reshaping how products are discovered and evaluated across digital channels. This shift is visible inside traditional search, where Google’s AI Overviews sit above paid and organic listings, and outside it, as shoppers turn directly to ChatGPT, Perplexity, or new AI search engines for advice. Instead of short keyword queries and long results pages, users now ask conversational questions such as “what’s the best skincare for sensitive skin?” and expect a curated, guided response. Product discovery is moving from list-based browsing to AI-led recommendation, compressing research, comparison, and selection into a single, dialogue-driven journey that can make or break a brand’s visibility in seconds.
Google, Amazon and the Rise of LLM-Based Product Discovery
Google and Amazon are racing to build LLM-based search experiences that act more like shopping assistants than indexers of links. On Google, AI Overviews summarise options and surface a small set of citations before users ever see a full results page, while major product discovery platforms within ecommerce sites are embedding conversational agents that help users narrow choices with natural language prompts. Users treat these experiences as guided conversations: they ask follow-up questions, refine preferences, and step closer to purchase without sifting through dozens of blue links. Yext’s 2025 study found that 62% of consumers trust AI to guide their brand decisions, even if they still cross-check results. At the same time, SparkToro’s 2024 research shows that nearly six in ten Google searches end without a click, which means brands may be judged by AI answers long before any visit to their site.
Why Traditional SEO Alone No Longer Protects Brand Visibility
Classic SEO and keyword optimisation assumed users would scan multiple results and click through to compare products. In an AI search environment, that logic breaks. Large language models compress research into one or two synthesized answers, where only a handful of brands are cited and even fewer are explicitly recommended. Citations and recommendations are different: a brand might appear as a reference in an AI-generated answer but be absent from the shortlist of suggested products. Google’s guidance on generative AI features confirms that high-quality, relevant content still matters, yet the balance has shifted from exact keyword matches to relevance, intent, and completeness of information. As zero-click behaviour grows, brands must treat AI search engines as primary discovery channels, understanding not only rankings in classic SERPs but their share of voice inside AI summaries that many users may never click beyond.
Emna.ai and the New Toolset for AI Search Optimization
New tools are emerging to help brands regain control in this opaque landscape. Tradedoubler’s Emna.ai focuses on AI search optimization by connecting directly to major LLMs and running brand-level market insights around the prompts people use at different funnel stages. It calculates a brand’s share of voice in AI-generated answers, identifies which articles, domains and publishers are cited, and compares performance with competitors. Emna.ai then links measurement with execution: it supports content creation in a brand’s tone, activates publisher campaigns, and tracks how share of voice shifts over time across specific prompts. In an early skincare campaign, a client moved from outside the top five to number 4 in France, while AI visibility rose from under 5% to 30% in under two weeks. The goal is a continuous improvement loop where brands can see what influences AI answers and respond fast.
Building AI-Ready Content and Metadata Strategies
To stay visible on AI search engines, brands need content and metadata strategies designed for conversational, intent-led discovery. That starts with richer product information: clear attributes, detailed descriptions, and honest comparisons that LLMs can reuse in answer generation. Brands should support this with diverse content across multiple touchpoints, including owned articles, publisher features, reviews, and FAQs that mirror natural language questions. Understanding which prompts matter most, and how they map to the funnel, allows marketers to design content clusters that speak directly to them. Metadata should describe context, use cases, and audiences, not only keywords, to help models interpret relevance. Finally, teams must track share of voice within AI-generated answers, not only organic rankings, and treat tools like Emna.ai and similar product discovery platforms as core parts of their brand visibility strategy in an AI-shaped search landscape.