Aleyda Solis says AI search expands SEO, not replaces it

SEO is still the floor, but AI visibility raises the ceiling

Aleyda Solis’s new Moz guide makes a simple argument with big operational consequences: AI search does not replace traditional search, it widens the journey around it. That means the old essentials still matter, but the job now extends beyond ranking pages to earning a place in the answers AI systems assemble, quote, compare, and recommend.

The clearest takeaway is that the search playbook is not being torn up. It is being stretched. Solis frames AI search optimization as an evolution of search optimization, not a separate game built on shortcuts or hacks. In practice, that puts crawlability, indexability, technical clarity, and genuinely helpful content right back at the center of the work, even as the market adds new layers like query fan-out, brand representation, citations, and pre-click influence.

The fundamentals still decide whether you are even in the race

The guide insists that AI visibility begins with the same foundation that has always made websites findable: search engines and AI systems can only work with what they can access, understand, and trust. If a site is hard to crawl, difficult to index, or muddled in its technical structure, the brand is already losing before any AI-generated answer is written.

That is why the guide keeps returning to the basics. Helpful content still matters because AI systems still need material worth retrieving and grounding. Technical clarity still matters because machine systems cannot amplify what they cannot parse cleanly. And crawlability and indexability remain nonnegotiable because no amount of brand ambition can compensate for content that sits outside the reach of the systems doing the retrieval.

Why AI search changes the scoreboard

The big shift in Solis’s framework is not about abandoning SEO metrics, but about expanding them. Traffic still matters, but it no longer tells the whole story because AI systems can answer questions before a user ever clicks through to a site. In that environment, a brand can be present, invisible, or misrepresented without any of those outcomes showing up neatly in a standard analytics dashboard.

Solis’s guide says measurement has to grow up with the medium. The important questions now include whether a brand is retrieved at all, whether it is represented accurately, whether it is cited, linked, recommended, compared, and ultimately selected inside AI-generated answers. That is a much broader definition of visibility, and it forces publishers and brands to think less like traffic chasers and more like source earners.

What AI systems are actually pulling from

The guide also reflects a crucial technical reality: large language models do not have to rely only on training data. They may use retrieval, grounding, web search, and other external sources to build answers, which means the web still feeds the machine even when the machine seems to speak on its own.

That is where Google’s own guidance matters. Google says its generative AI features in Search are rooted in its core ranking and quality systems, which is a strong signal that traditional SEO best practices still apply even as the results pages change shape. Google’s rollout of AI Overviews in the United States in May 2024, after users had already used them billions of times in Search Labs, showed how quickly those AI experiences moved from experiment to mainstream search behavior.

The new job: earn trust before the click

Moz’s broader AI search coverage points to the commercial stakes. New AI search features and generative engines are answering more questions directly in the SERP, which reduces traffic to websites. That creates a very specific risk for brands: if they are not referenced or trusted by the sources these systems rely on, they can disappear from the conversation even if their own pages still exist and rank in conventional search.

That is why the guide pushes beyond classic visibility toward pre-click influence. A brand now has to shape the answer environment itself, not just the landing page. Being discoverable is no longer enough if the model does not consider the brand worth mentioning, quoting, or comparing.

How to operationalize the guide day to day

Moz’s webinar page gives the practical spine of the approach. Solis’s session focuses on identifying the signals that influence AI visibility, what to optimize for in AI search experiences versus traditional search, how to prepare websites for emerging agentic features, and where to prioritize implementation for the biggest impact.

That is the right sequence for teams trying to make this real. First, identify which brand signals are actually surfacing in AI systems. Then separate the work that improves classic search performance from the work that improves AI-era representation. Finally, prepare for agentic features by making sure the site can support systems that do more than answer questions, systems that may act, compare, and choose on behalf of users.

The brand-ready framework and the team structure behind it

Solis’s guide adds a 10-characteristic framework for AI readiness, which turns brand visibility into something more concrete than a vague strategy memo. Even without turning that into a slogan, the point is clear: AI-ready brands need to be legible, credible, and consistent enough for models to recognize them across contexts.

That also explains the guide’s emphasis on closer integration between SEO, PR, and reputation teams. If AI systems are evaluating citations, mentions, and trust signals across the open web, then brand authority can no longer live in a single channel. SEO needs PR to shape what the web says about the brand, and reputation work needs SEO to ensure those signals are discoverable, attributable, and reinforced.

What teams should use to watch the new landscape

Moz is also building tooling around this shift. Its AI Visibility product can track brand mentions across major AI models including ChatGPT and Gemini, giving teams a way to watch how they show up when answers are generated instead of ranked in the traditional sense.

That kind of tracking matters because it helps translate a fuzzy strategic idea into a working workflow. If a team can see where the brand is mentioned, how it is framed, and whether it is appearing inside generative results, then optimization becomes less speculative and more editorial. The mission is not to choose between SEO and AI visibility. It is to use SEO as the base layer and then extend it into the systems that now mediate discovery, comparison, and trust.

Solis’s message is not that the old search playbook is over. It is that the playbook just gained a new chapter, and the publishers and brands that win will be the ones who treat AI visibility as part of search, not a detour from it.