Google is no longer the only thing that matters. AI systems are now answering questions about you and your business directly. Most people have no idea what those answers say or that they can be influenced.
Try this: open ChatGPT or Perplexity and type "tell me about [your name]" or "who is [your company] and are they reputable?"
What comes back is increasingly what people are using to make decisions about you. Not just search results they have to click through and evaluate. A synthesized answer, delivered with the confidence of an authoritative source, based on whatever these systems were trained on or can currently access.
If your online presence hasn't been built with AI systems in mind, the answer may be thin, inaccurate, or missing entirely. Any of those outcomes creates risk.
Answer Engine Optimization (AEO) is the practice of structuring your content and presence so that answer engines, the AI systems that respond to direct questions, represent you accurately and favorably. This includes how your content is written, how your website is structured, what schema markup is in place, and whether your information is consistent across the web.
Generative Engine Optimization (GEO) is the broader discipline of optimizing for generative AI systems, the large language models and retrieval-augmented systems that produce answers rather than lists of links. These systems pull from multiple sources and synthesize. If the sources they're pulling from are thin, outdated, or negative, the synthesis reflects that.
Both disciplines matter because the search landscape has fractured. People are looking for information about you in more places than ever, and the systems they use are drawing conclusions rather than just displaying results.
AI systems don't have opinions. They have training data and retrieval patterns. What they say about you is a function of what's available, how authoritative it is, and how consistently it appears across multiple sources.
If the most prominent content about you is a negative article from three years ago, that article influences the synthesis. If you have no owned content, no media placements, no structured data on your website, and no consistent information across platforms, the AI system has very little to work with and may default to whatever it can find, or simply say it doesn't have enough information.
That "not enough information" outcome is more damaging than people realize. In a high-stakes context, including an investor doing diligence, a board evaluating a candidate, or a potential client assessing a firm, an AI system that returns a thin or uncertain answer about you creates doubt. Not the same doubt as a negative article, but doubt nonetheless.
Building for AI search involves several things that overlap with traditional ORM but require specific attention.
Schema markup on your owned properties tells AI systems what type of entity you are, what you do, where you're located, and how you're connected to other authoritative entities. Without it, systems have to infer, and inference creates room for error.
Consistent entity information across the web means your name, title, company, and key facts appear the same way across your website, LinkedIn, media placements, directory listings, and platform profiles. Inconsistency creates confusion in AI training data and retrieval systems.
Authoritative third-party coverage gives AI systems something substantive to pull from. A placement in a recognized publication that describes who you are, what you've built, and what your expertise is becomes source material for AI-generated answers. Low-authority content doesn't carry the same weight in these systems.
Owned content that directly answers questions, covering what you do, who you work with, what your approach is, and what results you've produced, gives answer engines structured material to work with when someone asks about you.
The ORM industry developed its methodologies around traditional Google search. Page-one suppression, link building, content volume. These were built for a world where the primary question was "what ranks on page one of Google?"
That world still exists, but it's no longer the whole picture. The firms that haven't adapted their methodology to account for AI search environments are building campaigns that may perform well in traditional search while leaving their clients completely exposed in the places where high-value decision-makers are increasingly looking.
Every campaign I run is built for both environments simultaneously. The exact match domain, the authority content, the media placements, the schema implementation, all of it is structured to perform in traditional search and to give AI systems accurate, authoritative information to work with.
It's not a separate service. It's how reputation infrastructure should be built now.
Search your name in ChatGPT, Perplexity, and Google's AI Overviews. See what comes back. That's your AI reputation as it currently stands.
If the answer is thin, inaccurate, or missing, that's not a technical problem. It's a strategic one, and it's solvable with the same infrastructure that builds a strong traditional search presence. The work is the same. The intent behind it just needs to account for both environments.
If you want to know what those systems are saying about you and what it would take to change it, that's a conversation worth having.
I'll assess your current presence across both traditional search and AI environments and give you a straight read on what needs to change. Private, no pressure.
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