Search Is Fragmenting Quickly. Brands Need to Develop Knowledge Graphs

Search is undergoing a profound transformation. With the recent announcement of OpenAI's SearchGPT moving into beta, Google looking to insert ads into AI Overviews, and Bing revamping its own AI search results to include citations, marketers are scrambling to prepare for a completely new customer journey where classic keyword speak and reporting are likely to be nonexistent. Why is search fragmenting? Search fragmentation is merely the natural cycle of new technology adoption in the customer journey. These new technologies (LLMs, AI agents, AI search) are changing customer behavior and trust. In the early days of the internet, we saw a proliferation of search engines like Ask, Lycos, and AltaVista. Over time, this landscape converged, with Google emerging as the dominant player in what we now consider "classic search." However, we're now witnessing decentralization driven by AI and specialized search capabilities. This new reality aligns with a prescient observation made by Google and Deloitte back in 2017: "Brands are no longer competing with the best experience in their category; they are competing with the best digital experience a consumer has ever had." The challenge—and opportunity—for brands is to maintain a consistent, accurate, and engaging presence. It's not just about being discoverable on Google anymore; it's about ensuring that your brand information is accurate, up-to-date, and compelling, whether a consumer encounters it via a traditional search engine, an AI-powered search experience, an AI assistant, or any other emerging search platform. As search continues to fragment and evolve with AI at the forefront, marketers must prepare for a radically different digital landscape. This requires a fundamental shift in how we approach digital marketing and measure success. A new metric paradigm First and foremost, marketers should brace for a complete overhaul of traditional metrics. The keyword-centric approach that has long been the cornerstone of SEO and paid search strategies is becoming obsolete. AI-driven search systems, often not supported by traditional ads, render keyword targeting less effective. Moreover, conversational AI captures nuances and interests that keyword analysis simply can't match. As a result, the classic analytics marketers have relied on will likely decline in relevance and effectiveness. Redefining on-site experiences As traditional search metrics decline, the importance of on-site engagement will skyrocket. Every interaction on a brand's digital properties becomes critical. Marketers need to prepare for a future where visitors expect an AI-driven experience on par with their favorite AI assistants. This means moving beyond traditional navigation structures like drop-down menus and developing intuitive, conversational interfaces that can instantly guide users to the information they need. Privacy takes center stage The rise of AI in search is set to catalyze more stringent data privacy regulations. Marketers should expect a significant push for comprehensive data privacy laws that protect consumers' interactions with AI systems. This shift will necessitate a new approach to data collection and usage. Marketers must pivot toward zero-party data interactions, focusing on turning these into meaningful, trust-based first-party relationships with clear consent. The ability to navigate this new privacy landscape will be crucial for maintaining consumer trust and compliance. The trust factor With AI mediating many consumer interactions, brands that can establish themselves as reliable, accurate sources of information will have a significant advantage. This means not just being present across various platforms but ensuring that the information provided is consistently accurate, up-to-date, and valuable to the user. Adapting to conversational search Content strategies will need to evolve to answer complex, multifaceted questions—rather than simply targeting specific keywords—and require a deep understanding of user intent and the ability to provide contextually relevant information. While challenging, this transition also offers exciting opportunities for brands to connect with their audiences in more meaningful and impactful ways. Businesses must adopt a structured data approach within their organizations. This means bringing together typically siloed data stores that host the knowledge necessary to power AI experiences for customers. We're talking about entities like your business locations, facts about your products, information about services you provide, open job positions, frequently asked questions about your business, and specific details like allergens and ingredients in your menus—all hosted in a knowledge graph. A knowledge graph is a network of interconnected data points representing these real-world entities and their relationships. Its power lies in its ability to provide context between different pieces of information, which is essential for AI systems to understand and utilize your brand data effectively. Google and Bing have been building out the largest knowledge graphs to power search for more than a decade. Implementing a knowledge graph involves identifying key entities relevant to your business, defining their attributes and relationships, and managing this structured data in a system that keeps it up-to-date and accessible to various AI and search platforms. While you don’t have to use a knowledge graph design, the point is that you do need to incorporate a culture of structured data across your organization that identifies, stores, updates, and shares the critical knowledge every customer needs to know about your brand. There is a strong correlation between synchronized data and Google leading to your brand; in other words, sending the data ecosystem updates (like signals of life) has a statistically significant impact on clicks. Brands must recognize that their AI strategy is fundamentally their data strategy. Emerging AI systems rely on data consistency across platforms, both in training and in "grounding"—which, in the context of LLMs, refers to anchoring AI-generated responses in factual, up-to-date information. This is where knowledge graphs become critical: They provide a structured, comprehensive source of accurate brand information that AI systems can rely on to ground their responses. Along with data consistency, it's also critical to get your brand data everywhere and update it frequently for Google engagement. All initial indications and testing of Gemini show that if you can continue to power great search experiences, those are likely to be leveraged by AI systems. By maintaining a consistent presence, you're improving your visibility in traditional search and positioning your brand to be a reliable source of information for AI-powered search experiences. Today, brands can enter their data into one knowledge graph or database and have it update from a few to several hundred platforms in real-time. This is the fastest way to ensure not only update frequency, but also consistency across the entire data landscape where consumers engage with Search and AI. The key to all of these strategies is to view your data as a dynamic, living asset that needs constant care and distribution. By organizing your data in a structured manner and ensuring its wide distribution, you're preparing your brand to thrive in an increasingly fragmented and AI-driven search landscape. This proactive approach not only makes your information more accessible and manageable internally, but also positions your brand to meet consumers effectively at every possible digital touch point.

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Search Is Fragmenting Quickly. Brands Need to Develop Knowledge Graphs

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Christian J. Ward

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