AI-led product discovery is compressing the search journey
AI-led product discovery is the shift from keyword-based search results to conversational, large language model answers that guide users from research to purchase within a single, curated experience, reducing clicks, compressing consideration and rewarding brands that earn a place inside generative responses rather than on long results pages. Google and Amazon’s move toward AI-powered search means shoppers now ask natural questions and receive summary answers instead of scanning lists of links. Gartner predicts that traditional search volume will drop 25 percent by 2026, and Bain’s research finds that 80 percent of consumers rely on “zero-click” results at least 40 percent of the time. Within Google, AI Overviews sit above paid and organic listings, while outside traditional search, users start journeys in tools like ChatGPT and Perplexity. For brands, AI search visibility is becoming as important as classic SEO ever was.
From SEO to generative search optimization
As search engine marketing AI reshapes discovery, the rules of visibility are changing. Instead of ranking for short, transactional keywords, brands need generative search optimization that earns inclusion in multi-step, conversational answers. Users treat LLMs as guides, asking follow-up questions and narrowing choices without leaving the interface. SparkToro’s 2024 research shows that nearly six in ten Google searches in the U.S. and EU end without a click, turning AI answers into powerful gatekeepers for AI product discovery. Google’s own guidance says that best practices for SEO still matter because its generative AI features are rooted in core ranking and quality systems. However, brands now need to understand both citations and recommendations: appearing as a referenced source does not guarantee a model will recommend a product. Visibility means knowing which content, domains and reviews influence the final answer, and how that share of voice shifts over time.
NeuroRank and Model Preference Engineering for AI visibility
NeuroRank positions itself as an AI visibility intelligence platform built for continuous, governed AI search visibility. Its Model Preference Engineering subscription, starting from USD 225 (approx. RM1,050) a month, focuses on how large language models perceive, cite and recommend brands. Instead of only monitoring generative answers, NeuroRank diagnoses how models describe a brand, prescribes specific fixes, conditions the models through owned, earned and third-party content, and tracks month-on-month lift. The company defines this discipline as Large Language Model Optimization, a practice tested with 150 brands across 65 industries. Early results include a leading BFSI brand increasing AI visibility by 30 percent and citation frequency by 12 percent across systems like ChatGPT, Gemini, Claude and Perplexity, while a major FMCG brand improved AI visibility by 47 percent in 90 days. The platform’s aim is clear: give marketers hands-on control over how AI systems talk about their products.
Tradedoubler’s Emna.ai and LLM share of voice
Tradedoubler’s Emna.ai targets AI search visibility from a performance and publisher network angle, designed to explain what influences LLM recommendations for commerce. Emna.ai shows how a brand appears in AI-generated answers, measuring share of voice and mapping which domains and articles are cited, how often they surface and how relevant they are. It connects owned content, affiliate publishers and wider third-party sources to reveal which assets drive AI product discovery. The tool also distinguishes between being cited and being explicitly recommended, highlighting where a brand may be visible but not yet persuasive in the model’s final suggestions. With LLM-led journeys compressing browsing into single, guided responses, Emna.ai aims to give marketers concrete insight into which content deserves investment, how to improve generative search optimization and how their performance compares to competitors across AI-native surfaces like AI Overviews and conversational assistants.
New measurement and attribution challenges for marketers
The integration of advertising platforms with AI search experiences creates fresh measurement and attribution headaches. As zero-click results rise and AI assistants shape shortlists before users ever see a traditional results page, clicks and impressions no longer tell the full story of influence. Marketers need new metrics for AI search visibility, such as share of voice within LLM answers, frequency of brand mentions, and the gap between citations and recommendations. Platforms like NeuroRank and Emna.ai respond by exposing how often brands appear in AI outputs, which sources drive those mentions and how that visibility shifts over time. Yet tying these insights back to revenue will be complex as AI experiences sit between organic content, paid placements and affiliate ecosystems. For brands, the next phase of search engine marketing AI is not just about ranking but about proving how AI-driven discovery contributes to sales and brand preference.