AI Search 101: What It Is, Why It Matters and How Consumer Brands Win the New Shelf Space

What AI search is, why it matters and how to win the new shelf space. ## TL;DR AI search is how people now find, compare and decide inside AI assistants and chat experiences - not just on search engines. Instead of ten blue links, users get a reasoned answer, a shortlist and a nudge to buy. Winning here means making your products machine-readable, trustable and recommendable across models like ChatGPT, Gemini, Claude, Copilot, Perplexity and Amazon's Rufus. At Lmo7 we call this owning your AI shelf space. > **Update — May 2026:** Amazon has merged Rufus with Alexa+ to create **Alexa for Shopping**, now live on the Amazon Shopping app, website and Echo Show. References to "Amazon Rufus" in this post relate to the predecessor product. [Read Amazon's announcement.](https://www.aboutamazon.com/news/retail/alexa-for-shopping-ai-assistant) ## What is "AI search"? AI search is the shift from keyword matching to reasoning-first discovery. A user asks a natural question. The model plans the task, pulls signals from multiple sources and returns a contextual answer rather than a list of links. It blends search, comparison and buying advice into one flow. **Key traits of AI search:** - **Conversational.** Queries feel like chatting to a smart shop assistant. - **Goal-oriented.** Models plan steps - define needs, build a shortlist, evaluate trade-offs, pick. - **Multi-source.** Blends brand sites, retailer graphs, reviews, specs, PDFs and product feeds. - **Citations and actions.** Links, buttons and product cards appear inside the answer. - **Memory.** Follow-up questions refine the result. It's a different shape of discovery and it doesn't replace search - it sits on top of it. For most categories the consumer journey now looks like: Google frames the problem → an AI assistant clarifies the shortlist → Amazon or a retailer converts the order. ## Why it matters now **Attention has moved.** More journeys begin in AI environments. The latest research from [Graphite](/blog/ai-search-much-bigger-than-brands-think-2026) suggests AI now generates roughly 45 billion monthly sessions worldwide, with around 83% happening inside mobile apps. The lens of "ChatGPT web traffic vs Google web traffic" badly understates it. **The result is compressed.** Fewer visible slots. If you are not in the answer, you are invisible. Position #1 on Google now loses around 34.5% of its clicks when an AI Overview shows. **Speed to purchase is faster.** Clearer content and stronger proof translate to revenue more quickly because the shopper is closer to a decision when they engage. **Retailer AIs are rising.** Amazon's [Rufus](/blog/what-is-amazon-rufus-2026) is on track for $10 billion in incremental annualised sales. Products surface on reasoning paths, not keywords alone. ## How AI search works (a simple view) Take a real query: *"Best lightweight sunscreen for long runs in humid weather, under £20."* Here's what the model does behind the scenes. 1. **Reasoning path.** It identifies the constraints - SPF level, sweat durability, price, sensitivity, format. 2. **Signal gathering.** It reads PDPs, specs, FAQs, reviews, brand documentation, structured data ([Schema.org](/blog/what-schemaorg-foundational-guide-llm-optimisation-2026) blocks especially), retailer feeds. 3. **Synthesis.** It produces a shortlist with trade-offs and reasons. "Brand A: lightweight, low residue, slightly above budget. Brand B: budget-friendly, more residue, stronger sweat resistance." 4. **Action.** It provides links, add-to-basket prompts, store availability, or follow-up questions. If your content does not express attributes, outcomes and proofs clearly, you are skipped. The model can't extract what isn't structured to be extracted. ## Slugs and titles: the citation signal most brands miss When marketers think about optimisation, titles and meta descriptions usually get all the attention. There's another element, often overlooked, that plays a major role in both SEO and AI-driven visibility: the slug. A slug is the part of your URL that comes after the domain. In `lmo7.com/blog/ai-search-101-2025`, the slug is `ai-search-101-2025`. Good slugs act like a page label - they tell humans and machines what the content is about. As Large Language Models like ChatGPT, Claude and Perplexity increasingly cite web content, slugs have become a signal that can tip the balance between being referenced or ignored. **Why slugs matter for AI citation.** They provide clear context. Models scrape and interpret billions of pages and a clean, descriptive slug reinforces what the page is about - often more reliably than meta tags or body copy. They also reduce ambiguity. Vague slugs like `/page1` or `/article-2023` give no signal. Models prefer precision. **The pattern that gets cited.** Content with slugs closely aligned to the search query is far more likely to be cited. If someone asks "What are the best energy gels for marathon runners?", a page with `/best-energy-gels-marathon` in the slug stands a stronger chance of being referenced than one with `/our-energy-product-range`. Mirror the question. **Best practices.** Keep slugs short and descriptive (3–6 words). Use natural phrasing like "how-to" or "best-for". Strip filler words ("the", "and", "of") unless essential. Use hyphens, not underscores. For informational content, go literal (`/what-is-generative-engine-optimisation`). For commercial content, broaden slightly (`/best-lightweight-running-shoes`). The same logic applies to title tags. Models extract from the top of the page first and when they look for the page's "label", the slug and the title are the two strongest signals. Treat both as a bridge between your content and the questions real shoppers are asking. ## The Lmo7 framing: AI shelf space Your job is to earn and defend placement inside AI answers. We track this as **Share of Model** - the percentage of AI answers that name or feature your brand vs competitors. Think of it as SERP share, but for AI conversations. Most brands today have no Share of Model baseline. Setting one up is the first move. Without it, every optimisation is guesswork. ## The seven pillars of AI visibility We use a seven-pillar framework with consumer brand clients. Each pillar maps to a discrete piece of work. **1. Signal Architecture and Baseline Audit.** Audit brand visibility across ChatGPT, Gemini, Claude, Perplexity. Standardise product metadata across all endpoints. Implement structured data and ensure brand consistency. Output: a unified data foundation and baseline signal map. **2. Language Model Alignment.** Define query clusters with [Lexem.io](/blog/inside-lexemio-how-clustering-buying-intent-2025). Optimise copy for natural, conversational phrasing. Expand content to match real consumer intent. Output: language-tuned assets aligned with how LLMs retrieve. **3. Contextual Authority.** Secure mentions on trusted editorial and review sites. Publish crawlable FAQs and brand knowledge content. Seed expert and UGC discussions. Output: authority footprint across model-referenced sources. **4. Model Surface Monitoring.** Track brand recall and competitor presence across models. Benchmark shifts against the baseline. Flag visibility drops or misattributions. Output: a live dashboard of brand vs competitor mentions. **5. Optimisation Loops.** Run monthly tests to measure ranking shifts. Compare on-site and off-site changes with model baselines. Trial A/B content variants on high-impact queries. Output: refined assets and stronger semantic signals. **6. Visibility Leverage Points.** Pinpoint high-volume or high-impact queries. Target authority mentions and influencers. Syndicate content across influential channels. Output: priority actions with outsized visibility gains. **7. AI-Native Brand Positioning.** Shape USPs as direct answers to model queries. Refine a natural, conversational brand voice. Frame the brand narrative in model-agnostic terms. Output: a durable, AI-native brand story across models. ## Examples of AI-style queries to pre-answer The patterns brands need to be ready for: - "Sunscreen that will not sting eyes on a marathon." - "Steel toe boots that are ESD compliant for aerospace tooling." - "Hydration tablets for heavy sweaters, zero caffeine, UK delivery tomorrow." - "Compare these two models for plantar fasciitis, pros and cons for 10-hour shifts." Each one is a constrained, multi-attribute query that traditional keyword matching can't satisfy. The brand that wins is the one whose page surfaces the matching attributes clearly and quotably. ## Metrics that matter **Share of Model.** Percentage of relevant AI answers that feature your brand. **Coverage.** Percentage of priority prompts where you appear with a positive, actionable mention. **Sell-through lift.** Revenue change on target SKUs following content and structure updates. The whole point of the work. For more on how to measure AI search performance well - including what to retire from your old SEO dashboard - see [metrics to retire and what to track instead](/blog/metrics-to-retire-what-track-instead-2026). ## Common pitfalls - **Keyword nostalgia.** Stuffing terms without answering real missions. - **Inconsistent specs.** Conflicting weights, materials, or claims across channels. - **No proof.** Outcomes stated without tests, certifications, or quantified results. - **Hidden answers.** Burying crucial info in images only or vague lifestyle copy. - **Slow iteration.** Discovering gaps but not republishing quickly. ## FAQ **Is this just SEO with new clothes?** No. Classic SEO is about ranking pages. AI search is about earning inclusion in answers. Structure and proof beat word count. **Do backlinks still matter?** Authority helps, but coherent product data and evidence are what models quote. The shape of the work has changed - see our piece on [how AI search has shrunk the ranking that actually decides sales](/blog/ai-search-shrinking-ranking-decides-sales-2025) for the longer answer. **What is the fastest win?** Add FAQ-style answers to the exact buyer questions you see, mirror them on Amazon bullets and A+ and make them machine-readable with schema. That single move tends to outperform a quarter of generic blog work. **How do I know if it's working?** Run a fixed prompt set monthly. Score each response 0 (absent), 1 (mentioned), 2 (top recommendation). Watch the trend. Tie pillar-level changes to the metric that moved. ## Next steps If you want the practical playbook, read our companion guide on [what a good AI search strategy actually looks like for a consumer brand](/blog/good-ai-search-strategy-for-consumer-brand-2026). If you want help running the first audit, [Lmo7 can do that](/contact). That is AI search 101.