Is LLMO/GEO really different from SEO?

Your brand can rank on page one and still be invisible. This is the new reality of AI search, and it is already reshaping demand capture. If AI works differently, then optimization for AI search must be a completely new game that breaks away from SEO.

That assumption is costing brands visibility right now. The reality is sharper. AI search sits on top of the same foundations that modern SEO already uses. Useful content, authority signals, entities, internal linking, brand reputation. These are still the raw materials. What has changed is not the inputs. What has changed is how the system reasons about them and decides what to show.

Hence, if you are a CMO asking whether you need a separate “LLM optimization” playbook, the honest answer is this. You do not need a different universe of tactics. You need to upgrade how your existing SEO and content investments are evaluated in an AI first environment.

How did SEO evolve to this point?

To understand the shift, let’s rewind a little. Early search engines behaved more like primitive predictors. They matched words to words. If a page repeated the same term enough times, it ranked. That is where keyword stuffing and hidden text came from.

Google changed the field by asking a better question. Instead of only counting keywords, it measured the importance of a page through relationships between pages. That is what PageRank and backlinks did.

Then came the era of quality controls and meaning. Updates that punished thin or spammy content. Search began to understand intent, context, entities and topics rather than just strings of words.

Modern SEO is already about meaning, not just matching.
Which is why best practice today focuses on - 

• Clear structure
• Helpful content
• Topical and brand authority
• Internal linking
• Digital PR and brand mentions

In other words, SEO already moved from raw prediction to something closer to understanding. AI search is the next step in that same direction, not a separate planet.

Why are AI answers ignoring your brand?

Here is the uncomfortable pattern many CMOs are seeing. The brand ranks on page one for key informational and commercial queries. The domain has strong authority. PR, backlinks, topical depth, all in place.

Yet when someone asks an AI assistant for advice in that category, the brand is missing from the answer. This feels like SEO is failing but it is not in reality. What has changed is the question the system is asking before it replies. Traditional SEO optimization is built around retrieval.

These are some of the repetitive questions, your SEO team thinks about - 

• Can the crawler find the page?
• Can the algorithm decide to rank it for a query?
• Will a user click and land on it?

AI search engines change the final step. They do not stop at a ranked list. They try to answer the question inside the interface. That adds an extra evaluation layer between your content and the user. 

The algorithm rather asks these questions - 

• Can I use this source to explain the situation clearly?
• Does this brand fit naturally into the story I am building for this user?
• Is this source trustworthy for this exact context?

You can have excellent SEO and still be skipped if your content does not help the model search engineer its way through the problem the user is trying to solve.

How does AI actually decide what to say and who to cite?

The phrase “AI is just probability” is only half the story. There are two distinct steps under the hood.

First, the model tries to replicate a situation analysis. What is the user really trying to achieve. What constraints matter for this decision. What type of explanation will be most helpful right now.

Only after that internal reasoning does probability take over and generate the actual words. Sometimes the answer can come from what the model already knows from training. For simple definitional questions, it does not need to visit the web at all.

The moment the user asks about the best choice, current options, platforms, tools, or anything that depends on the real world, the model begins to pull from live sources.

In that moment, your content has to pass a new test. It is not only about whether you rank for “what is X.” The question becomes

Can this page help me construct a complete, confident explanation that feels tailored to this query. If the answer is no, even a high ranking page can be invisible in the AI answer.

What should CMOs change in their search strategy for the AI era?

The right frame is simple. Bad SEO will never win in AI search. Good SEO is now necessary but no longer sufficient.

To adapt your search strategy, you need to layer reasoning alignment on top of your existing SEO foundations. At a practical level that means five shifts.

1. Design content for scenarios, not only for keywords
   Map real situations your decision makers are in.
   “Which DMAT platform is right for a first time investor with low risk appetite” is a scenario.
   Build content that walks through choices, trade offs, constraints and recommendations. This is the type of thinking path AI systems look to mirror.
   
   
2. Write for explanation, not just ranking
   Pages that win in AI answers read like a clear conversation with a senior advisor.
   They set context, show the options, explain why a path makes sense and address obvious concerns.
   The goal is not only to match a query string but to help the system explain.
   
   
3. Strengthen entity clarity and topical authority
   AI models lean on knowledge graphs and entity relationships.
   Make it unambiguous who you are, what you are known for and which categories you lead.
   Consolidate scattered content into clear topic clusters so the model can see you as a go to source for that theme.
   
   
4. Align brand trust signals with the category story
   Trust is not abstract.
   In categories where regulation, safety or money are involved, the model looks harder at trust cues.
   That includes recognisable brand presence, consistent expert voices, third party mentions and clean factual accuracy across your content.
   
   
5. Build a search engineering mindset inside the team
   Treat prompts and AI questions as instructions to a reasoning system, not as longer keywords.
   Experiment with how assistants frame your category.
   Test your brand presence across multiple AI platforms and capture where your narrative breaks down.
   
   

Search is no longer only SEO or only LLM optimization. It is one stack where technical SEO, content, brand and reasoning aware design work together.

What is the solution for brands that want to win in AI search?

The solution is to treat AI search as an additional evaluation layer that sits on top of your current SEO, not as a replacement for it.

You keep investing in the right SEO fundamentals. You redesign critical content around user scenarios and decision journeys. You make your brand the easiest one for an AI system to use when it needs to explain a complex situation to a human. That is where visibility will come from.

Instead of asking “Do we need LLM optimization” the better CMO question is
“How do we make our existing search investments ready for a reasoning first engine”

Answer this well and you will show up wherever your buyers ask for help, whether that is a classic results page or an AI assistant.