Search engines no longer limit users to a box with 10 blue hyperlinks. The advent of large language models (LLMs) has profoundly changed how people locate information and evaluate choices, in addition to influencing their perception of brands. People are using full questions to find answers in context instead of entering disjointed keyword phrases. This is not just a new phase in searching for information; it represents a major change that requires an entirely new discipline called Search Engineering.
Traditionally, SEO focused on ranking mechanics as it pertains to keyword terms, links back to a web page, and technical hygiene (the process of optimizing a website to improve the quality of its content and make it more valuable to visitors). Although, these fundamentals are still relevant, they have become less important than they once were. Because the experience of working with an LLM-powered system, such as a chatbot or via generative search tool, involves much more than simply ranking web pages; it entails how those systems interpret, summarize and recommend the most relevant results based on a user’s request, brands are competing not only to be in the top ranked positions but also to be recognized as a credible source of information.
Search Engineering is the intersection between content, data and machine comprehension to create an effective digital presence that is aligned with the way AI-based systems retrieve and consume information. Optimizing for semantic relevance instead of emphasizing keyword density, building authority at the entity level, and ensuring consistency across all digital touchpoints are key to how brands will evolve in the coming months and years to address the shift from a keyword-driven to intent-driven approach.
One of the most significant changes in this shift is from target keywords to intent modeling. Brands have historically focused on creating content that is keyword-driven, but LLMs (large language models) are trained to develop a better understanding of context, nuance, and user intent. To build an effective content ecosystem, brands will need to create content that serves users throughout their entire journey, rather than creating isolated pieces of content based on just one keyword.
A well-ranked single piece of content is now less relevant than a large number of high-quality pieces of interrelated content, as they collectively show the expertise of the content creator through their connection to each other.
Another critical aspect of this shift is the establishment of entity authority. In order to understand the relationships between brands/topics/concepts, search engines and LLMs increasingly utilize structured knowledge graphs. Entity authority can affect how a brand is perceived as an entity based on whether or not there are consistent signals between owned, earned, and shared media, such as mentions of the brand in reputable publications and usage of structured data, as well as creation of authoritative content. If there is inconsistent or fragmented signaling, a brand is likely to experience diminished levels of recognition resulting in reduced chances of being included within responses to queries generated through AI.
The design of content is another critical factor when considering search-engine optimization. This does not mean inventing ways to optimize the search engine algorithms while neglecting the human experience. It means building a logical way to read a search-engine’s content as well as be utilized by human beings. Clearly defined headings, information organized in logical sequence, factual and accurate information, and concise text will increase the likelihood of being referenced/cited by LLM’s and, as a result, increase the total number of times your brand is extracted from your abs.
Measurement is evolving as well. Standard metrics (e.g. ranking and click through rates) do not provide a complete picture of visibility within an AI-driven ecosystem. Brands need to begin to measure where their brand is referenced in AI-generated responses, how many times they’re referenced, and/or share of voice for that brand in the responses. To do this requires the development of new tools and new measurements and the desire to change the definition of success.
Ultimately, search-engine optimization is about aligning the brand strategy with how search is actually done today. In order to form an aligned (i.e., cohesive and logically easy to read) digital presence for the brand, it is imperative that all horizontal disciplines (Content/PR, SEO, Data) collaborate in creating a unified site for the brand. Brands that have completed the full transition will not only be discovered but will additionally be capable of creating ongoing searching relationships.
LLMs are changing the way we view digital visibility and Search Engineering is one way brands maintain their relevance amidst this change.
