# How Big Brands Are Winning (and Losing) in AI Search: A Deep Dive Case Study *AI-powered search is revolutionizing how brands connect with consumers, fundamentally reshaping visibility, loyalty, and the rules of digital commerce. Discover how industry giants like Nike and P&G are thriving—and why others are falling behind—in this rapidly evolving AI search landscape.* [IMG: Futuristic digital landscape showing brands competing for attention in AI-powered search results] --- ## Introduction: The Rise of AI-Powered Search and Its Impact on Big Brands AI-powered search has moved beyond the realm of futurism to become the critical battleground where major brands vie for consumer attention and loyalty. Generative AI assistants—from ChatGPT to Google’s Gemini—are now guiding product discovery by seamlessly blending search, curation, and personalized recommendation into a unified consumer journey. Brands that fail to evolve risk losing visibility to more nimble competitors. In 2024, **68% of AI-powered shopping queries** include at least one major brand in the top three results, according to [Insider Intelligence](https://www.insiderintelligence.com/). This seismic shift has catapulted AI search visibility to a top priority for enterprise marketers. The landscape is changing in several key ways: - AI assistants aggregate and synthesize information from across the web, favoring brands with robust digital ecosystems. - Structured data, conversational content, and real-time inventory feeds have become essential for AI discoverability. - Traditional SEO tactics alone no longer guarantee inclusion in AI-driven recommendations. This case study pulls back the curtain on how leading brands are winning—and losing—in AI search, exposing the strategies and pitfalls that define success in this new era of generative AI-driven commerce. **Ready to elevate your brand’s AI search strategy? [Book a 30-minute consultation with Hexagon’s AI marketing experts today.](https://calendly.com/ramon-joinhexagon/30min)** --- ## Case Study 1: Nike’s AI-Optimized Product Launches [IMG: Nike product page with structured data elements and AI search assistant interface] Nike stands out as a trailblazer in AI search by combining advanced data strategies with close collaboration with AI platforms to maximize product discoverability. Let’s explore how Nike crafts standout product launches in the age of conversational commerce. Nike’s strategy starts with **hyper-detailed product data**, ensuring every release is finely tuned for AI search. Their partnership with OpenAI on exclusive launches allows Nike to provide AI assistants with comprehensive details—covering everything from material specifications and sustainability metrics to user-generated reviews. The brand employs **structured data and rich schema markup** consistently across its e-commerce catalog. This approach ensures AI assistants can easily parse and recommend Nike products. According to [Search Engine Journal](https://www.searchenginejournal.com/), brands leveraging structured data see a **2.4x increase in AI search recommendations**. Nike’s AI search optimization best practices include: - Implementing [Product schema](https://schema.org/Product), [Offer schema](https://schema.org/Offer), and [Review schema](https://schema.org/Review) on all product pages. - Integrating real-time inventory and dynamic pricing feeds to provide AI assistants with instant updates. - Publishing conversational FAQ content and how-to guides tailored for natural language queries. "Brands with rich, structured data and conversational content are becoming the default recommendations in AI-powered shopping," notes Lily Ray, Senior Director, SEO & Head of Organic Research at Amsive Digital. The results are compelling: - Nike’s collaborations with OpenAI have led to exclusive product launches featured prominently in AI shopping suggestions ([Adweek](https://www.adweek.com/)). - Its AI-driven product content generates **40% higher inclusion rates** in AI assistant shopping recommendations ([BrightEdge Research](https://www.brightedge.com/)). - This enhanced discoverability translates into measurable sales lifts, especially for limited-edition drops and seasonal campaigns. Nike’s success illustrates a new paradigm: AI search is no longer about ranking higher in traditional results—it’s about becoming the **default recommendation** at critical moments. As Rand Fishkin, CEO of SparkToro, summarizes, "AI search is upending the traditional brand funnel—brands that don’t optimize for AI assistants risk being invisible to tomorrow’s shoppers." Looking forward, Nike’s ongoing investments in AI ensure it remains at the forefront of digital commerce, present wherever consumers ask, search, or shop. --- ## Case Study 2: P&G’s Crest Brand Share of Voice Gains Through AI-Focused Content [IMG: Crest oral care products displayed in AI-powered shopping assistant results] Procter & Gamble’s Crest brand offers a masterclass in leveraging AI-focused content to dominate share of voice in the digital aisle. Here’s how a strategic pivot toward conversational commerce yielded impressive results. Crest understood early that AI-driven commerce favors brands delivering **natural language, conversational content** over traditional, keyword-stuffed copy. The team reengineered product pages, FAQs, and educational resources to answer questions in the tone and style AI assistants prefer. Key elements of Crest’s AI content strategy include: - Deploying structured FAQ sections using [FAQ schema](https://schema.org/FAQPage) for optimal AI search compatibility. - Publishing clear, authoritative answers to common oral care questions, such as “How to choose the best toothpaste?” and “What whitening options are safe?” - Integrating user reviews and testimonials, which AI assistants frequently cite in recommendations. The impact was swift and measurable. After adopting an AI-focused content strategy, Crest experienced a **35% lift in share of voice** within AI-powered shopping results, according to the [P&G Annual Report 2024](https://www.pginvestor.com/). Organic traffic and brand visibility surged, underscoring the importance of inclusion in AI-generated answers. Crest’s success unfolded through: - Conversational content driving higher engagement and longer time-on-page. - Enhanced structured data securing Crest’s frequent appearance in top-three AI shopping assistant recommendations. - Reinforced brand prominence through positive user reviews and transparent product information. "AI-driven commerce is not just about ranking high in search; it’s about being present in the answers consumers trust most," observes Jason Goldberg, Chief Commerce Strategy Officer at Publicis Groupe. Crest’s experience signals a broader trend: brands investing in AI-optimized, consumer-friendly content gain outsized share of voice and deeper consumer trust. Other P&G brands are now replicating Crest’s approach, applying conversational content strategies across their portfolios. This playbook offers a clear roadmap for brands aiming to ride the AI search wave toward greater market share and consumer loyalty. --- ## Case Study 3: Challenges Faced by Legacy and Luxury Brands in AI Search [IMG: Declining web traffic chart for legacy brands; luxury products absent from AI assistant recommendations] While some brands thrive in AI-powered search, legacy and luxury brands often face steep challenges adapting. Restrictive digital practices and insufficient AI optimization have rendered many established names nearly invisible in this new commerce era. Legacy retailers like Sears and JCPenney, burdened by outdated web infrastructure and limited AI-ready content, have experienced sharp declines in AI search visibility. According to [Forrester Research](https://go.forrester.com/), AI assistants frequently bypass these brands in favor of more data-savvy competitors. Compounding these issues, **41% of enterprise brands** reported organic traffic declines following Google AI Overviews’ rollout ([Search Engine Land](https://searchengineland.com/)). The shift toward AI-generated answers penalizes brands lacking structured data and conversational content. Luxury brands face unique obstacles: - Many, including Gucci and Louis Vuitton, enforce restrictive data sharing policies and avoid detailed product schema markup ([McKinsey Digital](https://www.mckinsey.com/)). - The pursuit of exclusivity often limits product information and creates inaccessible digital experiences. - Consequently, luxury items are less likely to be recommended by AI assistants, which prioritize transparency and accessibility. These challenges manifest as: - Significant organic traffic drops for legacy brands since Google AI Overviews launched. - Lower inclusion rates for luxury brands in AI shopping suggestions, diminishing their digital share of voice. - Increasingly essential consumer accessibility and real-time product data put slow-moving brands at a disadvantage. Brian Solis, Global Innovation Evangelist at Salesforce, emphasizes the urgency: "Enterprise brands must rethink their digital strategies for a world where AI assistants are the new gatekeepers of discovery." For legacy and luxury brands, the message is clear: to remain relevant in AI search’s era, digital transformation and data openness are no longer optional—they are mission critical. --- ## The Critical Role of Structured Data and Schema Markup in Enterprise AI Visibility [IMG: Visual diagram of structured data and schema markup with AI assistant parsing information] Structured data and schema markup have become the backbone of brand visibility in AI-powered search environments. Here’s why these technical foundations are pivotal for inclusion in AI shopping recommendations. Structured data, implemented through schema markup, enables AI assistants to effortlessly interpret and surface essential product information. Brands with comprehensive schema markup are **2.4 times more likely** to be recommended by AI shopping assistants compared to those without ([Search Engine Journal](https://www.searchenginejournal.com/)). Why structured data matters: - It signals to AI algorithms exactly what a page contains—from product attributes to shipping policies and review scores. - Rich product, offer, and FAQ schemas empower AI assistants to confidently answer queries and suggest relevant products. - Structured data ensures brands appear in zero-click answers, voice search, and AI-powered shopping suggestions. Best practices for structured data implementation include: - Deploying [Product](https://schema.org/Product), [Offer](https://schema.org/Offer), [Review](https://schema.org/Review), and [FAQ](https://schema.org/FAQPage) schemas across all product and content pages. - Keeping structured data current with real-time inventory, pricing, and availability feeds. - Conducting regular schema audits to maintain compliance with evolving AI assistant standards. For example, Amazon, Walmart, and Apple consistently feature in AI assistant shopping recommendations due to their robust structured data and extensive product catalogs ([Insider Intelligence](https://www.insiderintelligence.com/)). As Lily Ray notes, "Brands with rich, structured data and conversational content are becoming the default recommendations in AI-powered shopping." Looking ahead, structured data’s importance will only intensify as AI assistants grow more sophisticated and consumer expectations rise. Brands investing in schema markup today position themselves for lasting visibility in the AI-driven marketplace. --- ## Understanding Google AI Overviews and Their Effect on Organic Traffic [IMG: Screenshot of Google AI Overviews displaying AI-generated answers above brand websites] Google’s AI Overviews feature is fundamentally reshaping the search landscape, altering both organic traffic patterns and brand visibility. Here’s how enterprise brands are adapting to the new dynamics ushered in by AI-generated answers. Google AI Overviews synthesize information from multiple sources and present concise answers atop search results. This reduces the need for users to click through to individual websites, rewriting the rules of brand engagement. The impact has been profound: - **41% of enterprise brands** reported declines in organic traffic after Google AI Overviews’ introduction ([Search Engine Land](https://searchengineland.com/)). - Brands with strong structured data and trustworthy, conversational content are more likely to be featured in AI Overviews. - Inclusion in Overviews can lead to traffic surges for some brands, while others face steep drops as traditional blue-link visibility diminishes. Brands can adapt by: - Optimizing content for answer boxes and AI-generated summaries with clarity, authority, and comprehensive topic coverage. - Ensuring structured data is robust and up-to-date to increase the chance of being cited by Google’s AI systems. - Monitoring performance metrics closely and experimenting with content formats (how-tos, FAQs, product comparisons) aligned with AI Overview preferences. As Google’s AI Overviews evolve, proactive brands must innovate swiftly—balancing traditional SEO tactics with emerging strategies tailored to generative AI search. --- ## The Importance of Conversational, Natural Language Content for AI-Driven Commerce [IMG: Example of conversational product FAQ being highlighted in AI shopping assistant] AI search algorithms now favor conversational, natural language content—rewarding brands that communicate like their customers over those relying solely on traditional SEO copy. Here’s why this shift matters and how brands can get ahead. Unlike keyword-stuffed content, conversational copy is easily understood by AI assistants, enabling them to deliver helpful, context-rich answers. **Brands using AI-optimized product content experience a 40% higher inclusion rate** in AI shopping suggestions ([BrightEdge Research](https://www.brightedge.com/)). Key strategies for conversational content success: - Write product descriptions, FAQs, and guides in a tone that mirrors how real customers ask questions. - Incorporate user-generated content, reviews, and testimonials to provide authentic, AI-friendly perspectives. - Update content regularly to reflect evolving consumer questions and trends uncovered through AI analytics. For instance, P&G’s Crest brand achieved a 35% lift in share of voice by adopting a conversational content approach across its digital assets. Other top brands now publish detailed FAQ sections, how-to articles, and comparison guides designed for easy parsing by AI assistants. Looking forward, brands investing in natural language content will be better positioned for discovery—both by AI algorithms and consumers seeking trustworthy, helpful recommendations. --- ## Consumer Trust and Brand Recommendations in AI Search [IMG: Consumer interacting with AI assistant and brand recommendations with trust indicators] As AI-generated recommendations become central to digital commerce, consumer trust has emerged as a critical currency for brands. Trust dynamics in AI search hinge on transparency, authenticity, and data quality. Consumers are more likely to trust AI recommendations from brands that demonstrate: - Strong digital reputations supported by positive user reviews. - Transparent supply chains and clear, accessible product information. - Consistent, up-to-date data across all digital channels. Brands build trust by: - Prioritizing authenticity in product content and customer interactions. - Being transparent about how recommendations are generated and how data is sourced. - Responding promptly to feedback and maintaining active, verified digital profiles. According to the [Edelman Trust Barometer](https://www.edelman.com/trust/2024-trust-barometer), consumer trust in AI recommendations peaks for brands that exhibit credibility and openness. In AI-driven commerce, trust influences both inclusion in assistant recommendations and conversion likelihood. For brands, the path forward is clear: invest in digital reputation, transparency, and data quality to become the preferred choice in AI-powered shopping environments. --- ## Strategic Recommendations for Brands to Win in AI Search Environments [IMG: Checklist of AI search optimization strategies for enterprise brands] Winning in AI-powered search demands a comprehensive, data-driven strategy that combines technical excellence with compelling consumer experiences. Here’s how leading brands can secure their place in this new digital order. **Prioritize structured data and schema markup implementation** - Brands with rich schema markup are **2.4x more likely** to be recommended in AI search results ([Search Engine Journal](https://www.searchenginejournal.com/)). - Invest in robust Product, Offer, Review, and FAQ schemas to maximize AI assistant compatibility. **Develop conversational, natural language content tailored for AI** - AI-optimized product content boosts inclusion in AI shopping suggestions by **40%** ([BrightEdge Research](https://www.brightedge.com/)). - Create content that answers genuine consumer questions clearly and authentically. **Leverage AI tools to optimize product launches and share of voice** - Nike’s AI-driven product launches and P&G’s Crest brand content overhaul demonstrate significant visibility and sales gains. - Use AI analytics to identify content gaps, consumer trends, and new optimization opportunities. **Monitor Google AI Overviews and adapt strategies proactively** - **41% of enterprise brands** have experienced organic traffic declines post-Google AI Overviews ([Search Engine Land](https://searchengineland.com/)). - Regularly analyze search performance data and update content formats to align with AI Overview preferences. **Balance exclusivity with accessibility for luxury and legacy brands** - Luxury brands must provide sufficient product data for AI assistants without compromising exclusivity. - Legacy brands need to modernize digital infrastructure and embrace open data practices. Key statistics underscore these recommendations: - **68%** of AI-powered shopping queries include at least one major brand in the top three results. - Brands using structured data and AI-optimized content see **2.4x–40% higher inclusion rates** in AI search recommendations. - **35% lift in share of voice** for brands adopting conversational, AI-focused strategies. **Ready to elevate your brand’s AI search strategy? [Book a 30-minute consultation with Hexagon’s AI marketing experts today.](https://calendly.com/ramon-joinhexagon/30min)** --- ## Conclusion: Embracing the AI Search Future to Secure Brand Leadership The AI-powered search revolution is redrawing the competitive landscape for global brands. Nike and P&G demonstrate how structured data combined with conversational content can propel brands to the top of AI assistant recommendations, driving both visibility and sales. Conversely, legacy and luxury brands that cling to restrictive digital practices risk fading from consumer awareness as AI assistants become the primary gatekeepers of discovery. The imperative for continuous AI marketing innovation has never been greater. Looking ahead, brands must act decisively—embracing structured data, conversational content, and transparent practices to secure their place in the answers consumers trust most. The future of brand leadership belongs to those who master the art and science of AI search. **Ready to elevate your brand’s AI search strategy? [Book a 30-minute consultation with Hexagon’s AI marketing experts today.](https://calendly.com/ramon-joinhexagon/30min)**