AI Is Revolutionizing SEO. Here’s How Marketers Can Keep Up.

The search engine optimization space is experiencing fundamental change with the introduction of generative AI that begins to influence how people search, how platforms present content and how brands become visible online. Numerous people swoop in to declare themselves masters of AI SEO, but few of them have taken the time to learn about how these models operate.

Over the past few months, I spent time studying generative AI through formal training, industry webinars and tech forums. During one particularly insightful session, an MIT professor described such models as a “statistical lottery.” Unlike traditional search algorithms that execute a fixed set of rules, LLMs generate output based on probability. This means answers to the same query may be different each time.

This is not a bug. This is intentional. Rand Fishkin, Founder of Moz and Co-founder of SparkToro wrote this last month when talking about the non-repeatability of responses in LLMs. The difference is an advantage, not a disadvantage.

Related: Is Your SEO Strategy Ready for the AI Search Engine Takeover? Act Now — or Risk Getting Left Behind.

SEO tools are evolving, but they are not that easy to access

Some of the leading SEO tools are starting to monitor AI visibility. Semrush and Ahrefs, for example, now have features that statistically predict how often a domain appears in AI responses. But their measures are derived from third-party modeling. From my knowledge, they don’t actually see into the internals or datasets of the language models themselves. This was confirmed in a recent podcast interview I had with Benj Arriola of Clarity Digital.

This is significant. Marketers ought to understand that the vast majority of AI visibility metrics are not directly from the models. They are indirectly known through outside observation and statistical modeling.

Reconceiving SEO metrics in the AI era

If you care about improving your visibility within AI-generated results, the beginning is understanding the character of the medium. Generative AI requires new KPIs that go beyond traditional rankings.

Instead of using AI placement in silos, measure:

• Large language model referral traffic

• Branded search or direct traffic growth or decline

• Brand visibility on the internet, including reviews and third-party sentiment

All of these metrics offer a better signal of actual-world visibility and brand impact within AI-driven contexts.

Classic search remains king

Even though the shift towards generative AI is in progress, it’s in the minority. Google still takes more than 90% of all search volume in the world, according to recent numbers reported by Barry Schwartz. Traditional search is what most users are still using, instead of chat surfaces.

So the foundation of SEO remains king. Technical health optimization, quality content and user intent are still the foundations for sustained visibility.

Related: Generative AI Is Changing SEO Forever — Here’s What You Need to Know to Stay Competitive

Evolving with generative search

Generative AI is not a passing fad. It is a reflection of a broader change in the way information is being processed, interpreted and transmitted across the digital landscape. For marketers, this shift is not just about tactical surface-level plays. It is more about having a better sense of how the systems work and a more strategic framework for calculating visibility and success.

This is where operational transparency comes into play. Marketers need to move past analog KPIs and start working with models that align with how AI models reveal and prioritize information. Visibility in AI-provided answers is less about specific keywords and more about overall signals of brand authority, relevance and openness.

To help with that transition, I created a free AI Search Readiness Self-Assessment. It is designed to help marketing teams evaluate their readiness in content, technical infrastructure, brand presence and analysis of data. It is not about checking boxes. It is about identifying what needs to be prioritized as the search landscape keeps evolving.

Traditional SEO remains valid. Strategies like technical SEO, quality content and user intent alignment remain the foundation. But long-term winners will be those who enter this new age deliberately, with an understanding of how generative AI works and not with a focus on short-term discoverability.

As things change faster, adaptability on the basis of clarity will separate the leaders from the followers.

The search engine optimization space is experiencing fundamental change with the introduction of generative AI that begins to influence how people search, how platforms present content and how brands become visible online. Numerous people swoop in to declare themselves masters of AI SEO, but few of them have taken the time to learn about how these models operate.

Over the past few months, I spent time studying generative AI through formal training, industry webinars and tech forums. During one particularly insightful session, an MIT professor described such models as a “statistical lottery.” Unlike traditional search algorithms that execute a fixed set of rules, LLMs generate output based on probability. This means answers to the same query may be different each time.

This is not a bug. This is intentional. Rand Fishkin, Founder of Moz and Co-founder of SparkToro wrote this last month when talking about the non-repeatability of responses in LLMs. The difference is an advantage, not a disadvantage.