Search engine optimization (SEO) has been around for decades. Over that time, algorithms have evolved, ranking factors have shifted, and entire industries have grown around getting to the top of the results. But the rise of large language models (LLMs) and AI-driven assistants is accelerating a new kind of change.

How SEO is Changing with AI  

Instead of sifting through lists of links, consumers are increasingly turning to tools like ChatGPT or Google’s AI-powered search to get direct, conversational answers. Beyond that, these tools are becoming embedded in browsers and enterprise applications. This shift is forcing organizations to rethink how they manage their digital presence.  

The question is no longer, “How do we rank on Google?” but also, “How often do we show up when a customer asks Claude about our space?”. 

Organizations are already beginning to test how they show up inside these new AI ecosystems. It’s the early days of AIO (AI optimization), GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), or GSO (Generative Search Optimization), where companies are feeling their way towards similar performance playbooks that once defined SEO. 

How LLMs Differ from Traditional Search 

LLMs don’t operate like traditional search engines. Rather than directing users to a list of websites, they generate a synthesized answer that draws from multiple sources. Many use retrieval-augmented generation (RAG) to pull in real-time content from the web and weave it into their replies. This creates a dynamic experience where the model can search for and interpret results. The answer served to the user is based on semantic similarity, rather than solely on keywords. 

The result is a move away from the familiar experience of 10 blue links on a page. Instead, users often see a single text response, with far fewer opportunities for individual brands to drive clicks. Even when a website’s content is used, it is usually transformed, summarized, and reframed. Adding to the challenge is the black-box nature of these models. Much like the early days of SEO, the rules are not yet clear, and organizations are experimenting without knowing exactly how rankings or content sourcing are determined. 

How Organizations Should Navigate This Shift 

Despite the uncertainty, there are practical steps that marketers can take today.  

Traditional SEO 

Traditional SEO remains important, but its role is shifting. Strong search visibility can still drive being a source the LLMs pull from, yet it may not be sufficient on its own. Many AI systems pull from a broader range of materials, like forums, open datasets, and social media, so traditional SEO should be seen as necessary but not the whole picture.  

In particular, long-tail keywords are emerging as a key bridge between old and new search models, reflecting the natural language people use when interacting with AI tools. By targeting these nuanced, intent-rich phrases, organizations can strengthen both conventional SEO performance and alignment with conversational AI behavior. 

Structured Metadata  

Organizations need to double down on structured data within their webpages. These elements are not visible to page visitors but are crucial for search engines. Machine-readable elements such as metadata, schemas, and topical hierarchy help models interpret and trust site content. This increases the likelihood that your content will appear in the summarized answer to a chatbot prompt. 

Unique Content 

Like SEO, unique content drives results in LLM search. Brands that invest in original insights, proprietary data, and case studies will stand out to both humans and AI systems, increasing the chance that their content is included in a reply. Providing the same stale content that echoes what other sources have shared doesn’t drive traffic or results. The brands offering something original and new are the ones that will surface. 

High-Quality Content 

Since LLMs interpret context rather than just keywords, content quality matters more now than ever. Google continues to emphasize its E-E-A-T framework (experience, expertise, authoritativeness, and trustworthiness) to evaluate whether material is valuable. Content that is up-to-date and authored by experts in a particular area signals reliability to LLMs and helps surface the content to human readers. 

Human Readable Text 

Equally important is writing in a way that works for both people and machines. Clear formatting, thoughtful headlines, and scannable sections not only improve the reader’s experience but also make it easier for chatbots to parse and reuse information. List formats can also assist with this. They break down complex and lengthy paragraph content into smaller, more digestible thoughts that LLMs can better match to prompts, increasing the likelihood of including them in replies.  

FAQ-Like Content 

The conversational nature of these platforms also means organizations need to anticipate how customers might phrase questions. A person may ask, “What’s the best option for sustainable sneakers?” rather than typing “eco-friendly sneakers brand.” Shaping content around natural language queries and expanding FAQ sections can help align with this kind of user behavior.  

Testing 

In parallel with these changes, companies should actively test how their brand appears within AI platforms. Asking ChatGPT, Gemini, Claude, Perplexity, or other models about your company provides a snapshot of how your reputation and content are being interpreted today. Beyond directly using these tools, there are emerging platforms that attempt to measure your AI visibility and brand appearance across multiple AI sites. 

Watch Outs and What’s Next 

The transition to AI-driven search is not without risk. Compliance and ethical considerations are growing, as regulators explore how AI can be trained and what kinds of content use are permissible. The gap between what is technically possible and what is ethically advisable will only widen, requiring organizations to make deliberate choices about how they engage with these tools. 

Another area to watch is the commercial side of search. Just as traditional search results evolved to include sponsored listings and featured content, partnerships may emerge between chatbot platforms and brands. Being featured within an AI response could carry the same weight or more than a top search ranking once did. 

Lastly, marketing measurement strategies will have to shift. It’s unclear how AI-driven search can provide reliable attribution back to the source or indicate when a brand’s content is used. This impacts marketing metrics. How should marketers measure ROI if chatbot impressions replace clicks? Referral traffic from the new chatbots can be tracked, but unless a user clicks on a link served by a chatbot, that view may not be captured.  

Organizations are exploring other options, but the lack of consensus on defined metrics leaves these questions unanswered. 

There is no definitive playbook for SEO in the age of LLMs. However, the organizations that will succeed are those that pay attention, experiment early, and adjust as guidance emerges. Maintaining strong SEO fundamentals, auditing how content appears in AI-driven tools, and staying current on regulatory shifts are all essential components of this process. 

If you’re curious about how these changes could impact your organization, reach out to our digital experts.