Mastering generative experience optimization: new pathways for organic growth

The concept of SEO is constantly evolving, with the latest iteration being Search Experience Optimization, or GEO. This evolution signifies a fundamental change in how users search and what they expect from results. A decade ago, users typed queries expecting a list of “blue links,” but this began to change three years ago, and now, with GEO, there’s an expectation for more precise answers, especially for informational queries. User queries have transformed into prompts, seeking concise answers rather than a long list of links to sift through. This aligns with Google’s long-held ambition, articulated by Larry Page in 1998, for a machine learning engine that provides a single, understood result.

GEO (Generative Experience Optimization) is an evolution of SEO, not merely a new name. It perfectly aligns with Search Experience Optimization by shifting the focus from just ranking to being the chosen source in AI-driven experiences. It is not only about visibility but also about influence and inclusion in AI outputs. Brands serious about organic visibility are now recognizing that SEO is no longer solely about generating clicks. They consider top-of-the-funnel brand impressions as crucial; even if a user does not click a link, seeing a brand’s name associated with an answer in a Large Language Model (LLM) can build recall for future purchasing decisions. This shift means that while impressions might increase, clicks might decrease, impacting traditional Click-Through Rate (CTR) metrics. However, the clicks that do occur are from users with much higher intent, leading to more qualified leads and potentially higher conversion rates. This necessitates a holistic SEO and positioning effort to capture brand impact beyond immediate clicks.

We are optimizing for both the user and the AI, but primarily through the AI, as the AI becomes the intermediary. Therefore, we now need to structure and surface our expertise in ways AI models can interpret, trust, and cite. The website’s role shifts from being a mere destination to being a credibility engine. It becomes a source of verified authority, designed to power AI responses even when it does not receive a direct click. This involves ensuring the website is structured, up-to-date, and meticulously built to serve as a reliable information source for AI models.

A pertinent question arising from the rise of AI Overviews is the continued relevance of websites if users are no longer directly visiting them for content. While AI Overviews can summarize information, for actual transactions and conversions, users still typically prefer to visit a website to establish trust. This indicates that the current era is just the beginning of a new phase, and a complete shift to transactions solely through AI is not immediate. Furthermore, even if LLMs were to facilitate transactions directly, they would still require integration with and information from websites. LLMs need to crawl websites to understand user-generated content (UGC), product details, and after-sales services to make informed recommendations. The B2B world, in particular, is moving faster in its reliance on LLM recommendations. For example, platforms like G2 have reportedly seen significant traffic loss because businesses are now trusting AI models, trained on vast internet data, for recommendations instead of traditional review sites. Personal stories show this, such as an LLM providing more detailed and helpful answers to tax and medical queries than human consultants, highlighting the growing capability of LLMs to provide comprehensive and often superior informational answers.

The new, most important KPIs for SEO now include inclusion in AI Overviews, citation frequency in LLM outputs, brand mentions in vector search, helpfulness score in Google’s ranking systems, and structured data indexation. While traditional metrics still matter, they are becoming less central. Consequently, clients’ priorities and requests have significantly changed since AI Overviews became prominent. Clients are now asking, “How do we get into the AI box?”. They are more focused on information architecture, first-party data, and reputation signals. Additionally, they are prioritizing expertise showcasing over traditional keyword stuffing.

To stay relevant, a modern SEO professional needs to master several specific new skills. These include prompt engineering, schema markup at scale, vector search understanding, entity-based optimization, and understanding how LLMs interpret authority. It is also becoming vital to know how to collaborate effectively with content strategists and data engineers. To convince a leadership team to invest in GEO when they are still focused on traditional SEO metrics like rank and traffic, the conversation needs to be reframed. The argument should be: “Search is evolving. If you’re only tracking clicks, you’re missing where influence now happens, at the top of the funnel, inside AI responses. GEO ensures you are not invisible in the future of search”. This approach highlights the long-term strategic importance of GEO beyond immediate click-through rates.

There is a massive opportunity to gain visibility from LLMs, as not all brands are currently optimizing for these new parameters. The fundamental approach involves optimizing content not just for users, but crucially, for machines that consume and present this content. This means content needs to be conversational and provide precise answers to user queries, enabling machines to easily identify the best answer. The focus has shifted from merely optimizing for keywords on a landing page to comprehensively answering user questions. Websites that already adhere to practices of answering user questions are performing well in LLM traffic with minimal changes.

Several factors make a significant difference in gaining LLM visibility: User-Centric Content involves writing content that directly answers user questions, moving beyond keyword stuffing and long, irrelevant narratives. This marks an improvement from an earlier “bad shape” of SEO, where finding relevant information on Google was challenging due to keyword-stuffed articles. Structured Data (Schema Markup) has become more critical than ever; the web is an unorganized space, and structured data helps crawlers understand content by explicitly defining elements like FAQs, user profiles, ratings, and product details. This technical optimization layer significantly eases the machine’s ability to digest website information. Core Web Vitals, or website performance, is paramount, as LLM crawlers will not wait indefinitely for content to load. Ensuring content is displayed as quickly as possible through good Core Web Vitals is essential for content consumption and indexing. Finally, Information Architecture plays a huge role in helping LLM crawlers understand and navigate a website effectively. Beyond traditional KPIs like clicks and conversions, brands are now closely monitoring impressions from “position zero” and AI citations, which are brand mentions within AI Overviews. The ultimate focus remains on leads generated, conversions, and the overall impact on revenue, regardless of whether the traffic originates from traditional search or LLMs.

Both short, factual “nuggets” and comprehensive, long-form pillar pages win in GEO, but they serve strategically separated purposes. Nuggets are effective for AI inclusion because they are easily quotable. Pillar pages, on the other hand, build the topical authority behind the scenes. The approach is to think of it as modular content: snackable formats built on deep foundations. When considering what is more valuable for GEO, unlinked brand mentions are becoming more powerful, especially when they are repeated across trusted sources. LLMs do not need a link to understand a brand’s authority; instead, they need context, consistency, and co-occurrence. This indicates a shift in how authority is perceived and processed by AI models, moving beyond the traditional reliance on hyperlinks.

Technical SEO remains foundational for GEO success. If a site’s crawlability, structured data, or content freshness signals are weak, AI may overlook it. Technical SEO now also includes understanding how structured content feeds LLMs and vector indexes. For large enterprises, the single biggest untapped GEO opportunity is owning first-party knowledge graphs. Most large brands have not yet structured their internal knowledge in a way that AI can readily consume. Building entity-rich, structured repositories internally will unlock significant future visibility. Additionally, there are key tactical differences between B2B and B2C companies when it comes to GEO. B2B needs to optimize for trust, depth, and decision-stage influence, aiming to be cited as a go-to source in niche domains. B2C, on the other hand, benefits from scale and sentiment, requiring lots of product data, reviews, and FAQs.

AI and automation are reshaping SEO teams by taking over repetitive tasks and freeing people to focus on strategy, creativity, and analysis. The real challenge is adapting; those who stick only to traditional keyword rankings risk becoming less valuable, while those who embrace new skills will thrive.

Key capabilities now include understanding how large language models and entities work, learning how vectors represent meaning, and developing strong prompt engineering skills to interact effectively with AI. Teams that can also build internal tools, like micro-crawlers or cannibalization detectors, gain an edge in managing large-scale projects. Programmatic SEO is becoming more important, too, allowing scalable content creation without sacrificing quality.

When it comes to AI-generated content, Google cares most about usefulness. The line between human and machine writing is blurring, and successful teams know how to refine AI outputs so they meet brand standards and deliver real value to readers.

One of the most impactful ways to use LLMs in SEO is by training them on proprietary content. This helps simulate how your brand might appear in AI-generated answers and reveals gaps in how your expertise is represented. LLMs can also be used to analyze and reverse-engineer competitor positioning within AI Overviews, offering valuable strategic insight.

A strong SEO or GEO tech stack today usually combines traditional tools like Screaming Frog and Ahrefs with AI-driven platforms such as Diffbot, Graphlit, or Writer.com. It also includes structured data tools like WordLift, vector database integrations such as Weaviate or Pinecone, and custom LLM workflows built on platforms like GPT or Claude.

Looking ahead, SEO will continue to evolve, but the core principle of thinking about users will remain paramount. The role of an SEO professional is to act as a “middleman” between machines consuming website content and the users, effectively presenting content to show that the user has landed on the right page and has everything needed for decision-making and conversions. This core aspect will likely never change. While search styles will continue to evolve, potentially incorporating voice commands for selecting products and even completing transactions, the underlying need to effectively present information to the user will endure. Voice search, which previously struggled due to technological limitations in understanding complex queries, is now catching up as LLMs can handle more conversational interactions, asking follow-up questions to provide precise answers. The future of search will likely be more conversational, moving beyond simple commands to more engaging interactions with machines.

My boldest prediction for search in the next 3-5 years is that traditional search results will shrink to less than 30% of total user interactions, with AI agents choosing the source, not the user. For SEO professionals feeling anxious about the future, the top piece of advice is to focus on becoming a “trusted node” in your niche’s knowledge graph. SEO is not dying; it is expanding into new terrain, and professionals should embrace this evolution.