From Keyword Lists to AI-Led Recommendations
AI-powered discovery in search means consumers use large language models to ask conversational questions and receive curated, answer-style recommendations instead of scrolling through long, link-based results pages. This is moving discovery away from traditional search engine results toward guided, dialog-based decision journeys mediated by AI assistants. Consumers now start with prompts like “what’s the best skincare for sensitive skin?” rather than short keywords, and use follow-up questions to refine options. Product research is shifting into LLM chats, inside features such as Google’s AI Overviews and in standalone tools like ChatGPT and Perplexity. According to Yext’s 2025 study, 62% of consumers trust AI to guide their brand decisions, even if they still cross-check some results. For brands, this compresses the classic funnel into a few AI-generated recommendations where inclusion matters more than position on a long results page.
The New Stakes of AI Search Visibility
As AI results compress browsing into a single answer, AI search visibility becomes a direct driver of brand perception and potential revenue. SparkToro’s 2024 research shows that nearly six in ten Google searches end without a click, which means many consumer journeys now begin and end inside zero-click, AI-shaped experiences. In this environment, a brand’s presence in an LLM answer can influence shortlists, comparisons, and final choices before users ever visit a website. Visibility is also more nuanced: a brand can be cited as a source without being the recommended option, creating a gap between awareness and preference inside AI outputs. Marketers therefore need to understand not only if they appear in AI answers, but how they are framed, which attributes are highlighted, and how often competitors appear alongside them in the same conversational context.
Emna.ai: Making LLM Influence Measurable
Tradedoubler’s Emna.ai is one of the first tools built to measure and improve AI search visibility across major LLMs. It connects to models such as those behind AI assistants and runs brand-level market insights around prompts asked at different funnel stages, then calculates a brand’s share of voice in AI-generated answers. Emna.ai shows which domains and articles are cited, how often they appear, and how relevant those sources are, spanning owned content, affiliate publishers, and broader third-party coverage. It also highlights where existing content supports key products and messages, and where gaps limit visibility. This gives marketers a way to see what is shaping AI answers in detail and to plan campaigns around high-value prompts, tying content creation, publisher partnerships, and generative engine optimization together in a feedback loop grounded in real LLM behavior.
BERA’s Brand Ranking LLM View Ties Visibility to Growth
BERA.ai’s new LLM Brand Rankings function inside its brand measurement platform adds a financial lens to AI-powered discovery. Instead of treating LLM presence as an isolated search metric, BERA aligns a brand’s ranking in LLMs like ChatGPT, Gemini, and Claude with its proprietary BERA Score and Love Curve. Marketers can see where brand equity and AI visibility align or diverge across categories, and which sources are defining how models describe the brand. The feature connects directly to BERA’s Brand-to-Business analysis to relate LLM visibility to sales, revenue, and enterprise value, turning AI visibility from a vanity metric into a leading indicator of growth. With new generative engine optimization integrations, the platform can also recommend actions to improve LLM rankings, making brand ranking LLM data part of a broader strategy rather than a static diagnostic.
Rewriting SEO and Content Strategies for LLMs
The rise of AI assistants is pushing marketers to rethink search engine optimization AI strategies around how LLMs read, cite, and recommend content. Google’s recent guidance on generative AI features stresses that “the best practices for SEO continue to be relevant,” meaning foundational work on relevant, high-quality content still matters. However, AI search visibility now depends on a wider mix of signals: detailed product pages, trustworthy reviews, expert comparisons, and publisher authority all shape how models answer prompts. Since citations and recommendations are not the same, brands must map which assets earn citations and which correlate with direct recommendations inside AI answers. Tools like Emna.ai and BERA’s brand ranking LLM capabilities give teams the visibility needed to adjust content portfolios, strengthen off-site signals, and close topical gaps so that when consumers turn to AI-powered discovery, the brand is not only mentioned but meaningfully preferred.