Three years since ChatGPT launched, and the search landscape has fundamentally changed. Google went from SGE to AI Overviews. Impressions are spiking but clicks aren't keeping up. Informational queries are being answered directly by AI, and traffic that SEO teams built careers on is evaporating. The game isn't about ranking anymore. It's about influencing what AI says about you.

There's been a lot of hand-waving that AEO (Answer Engine Optimization) is just SEO with a new label. Google likes to say "good GEO is just good SEO." And sure, in a lot of ways that's true. But there are core differences in how you think about the problem, and they matter. I've been testing an AEO playbook for months. Here's what's actually working.

Traffic Is Gone

Let's start with the uncomfortable truth. Traffic is falling off a cliff — not disappearing entirely, but declining steeply. AI Overviews answer queries directly, which means fewer clicks to your site. Google is routing complex queries to AI mode. The "plan a 3-day itinerary, family-friendly, indoor" type queries that used to produce ten blue links now produce a synthesized answer. This has been the model for 20 years and it's breaking.

Execs are breathing down SEO and content teams asking "where's our AEO strategy?" Everyone is scrambling. There are more questions than answers right now, but the direction is clear: search is becoming conversational, and conversational search doesn't send traffic the way keyword search did.

The Three Layers of an AI Response

To understand how to influence AI, you need to understand how AI constructs its responses. There are three distinct layers:

1. Training Data

The base layer. LLMs are trained on web text, scientific articles, books, Wikipedia — a massive corpus of documents with a knowledge cutoff. The latest Gemini models were trained on documents through January 2025. Training a new model is expensive, so there's always a significant delay between when content is published and when it enters the training data.

The variables that matter in this layer are domain and topic authority (classic SEO) and a newer factor: brand authority — how well your brand gets mentioned alongside topics and entities you want to be associated with. Brand sentiment also plays a role here in ways it never did in traditional SEO. Google Business reviews, G2 reviews, Capterra ratings, Reddit threads — how the ecosystem talks about your brand matters more than ever.

2. Research and Validation

This is where agents perform web searches before giving you a response. They're checking their work. AI performs web searches to prevent inaccuracies caused by outdated training data, to fill knowledge gaps between the cutoff and today, and to prevent hallucinations.

This is why sometimes ChatGPT searches and sometimes it doesn't — the model decides whether its existing training data is sufficient for your prompt, or whether it needs to validate with fresh information.

3. Memory and Context

The personalized layer based on your past conversations with AI chatbots. This is largely a black box — we don't fully know how AI uses past context or how it will evolve.

The Fastest Lever: The Validation Layer

Here's the key insight: the training data layer is slow and unreliable. You can publish content and hope it gets included in the next training run, but that's a bet with a 12-18 month payoff at best.

The validation layer, on the other hand, is where you can actually move the needle right now. If you can feed the agent relevant, up-to-date content while it's performing its research, you can influence the answer and win citations along the way.

This is the core of AEO: intercept the agent during its research phase. You're not waiting for training data updates. You're putting content where the AI will find it when it goes looking.

AI Web Searches Look Nothing Like Human Searches

This is where AEO diverges most sharply from SEO, and it's the insight that matters most.

In traditional SEO, you target keywords based on user search volume. You look at monthly searches, keyword difficulty, funnel position. You build topic clusters and hub-and-spoke models around what humans type into Google.

In AEO, you target the web searches that AI agents perform during their reasoning process. And these searches look fundamentally different from what users type.

When someone asks an AI "how can I optimize content for SEO?", the agent fans out into a set of web searches. Some look normal: "SEO content optimization best practices," "keyword research for SEO content." But others are bizarre: "content quality and E-E-A-T in SEO" — queries that no human would ever type, with zero monthly search volume. The AI is casting a wide net to catch as many relevant entities and embeddings as possible.

This means:

  • AI web searches often have no to low search volume. No human is typing these queries, so no SEO is targeting them.
  • No user engagement signals exist. Google's traditional ranking relied heavily on click data to determine quality. AI queries don't generate clicks, so the model has to weight other factors.
  • Entity relevance and topic authority become the primary ranking signals. Without clicks, the model falls back on how topically authoritative and entity-relevant your content is.

This is a wide-open field. The queries have no competition because nobody was optimizing for them. The old rules don't apply.

The AEO Playbook

Based on what I've been testing, here's the four-step playbook:

Step 1: Understand Your AI Topic Model

Ask Gemini and ChatGPT what they associate with your brand. "What are the core topics associated with [your brand]?" This gives you your baseline topical authority from the AI's perspective.

Then run a keyword clustering exercise to identify where your domain is strong and where it's weak across the topics you want to own. The gaps are your opportunity.

Step 2: Prompt Tracking (Not Keyword Tracking)

Instead of tracking keywords by monthly search volume, track the prompts people ask AI and the web searches the agent performs in response. Tools like Google AI Studio with grounding enabled let you see the actual reasoning chain — the specific web searches the model uses to validate its answer.

The key is to tackle the entirety of a target topic. When an AI fans out into multiple searches, creating content for each of those subtopics increases your brand's surface area for being cited.

Use "People Also Ask" and Google autocomplete as proxies for prompt volume, since there's no direct way to measure how many prompts happen for a given topic.

Step 3: Create Entity-Rich Content for Agents

Write content that targets the AI's web searches, not user keywords. Because these queries have no competition and no user engagement signals, the models weight topical authority and entity relevance more heavily than anything else.

This content behaves like normal content in every way — same formats, same publishing channels. The difference is what you're targeting and why.

Step 4: Influence the Response

Once you start earning AI citations, you can close the loop:

  1. Identify which of your pages get cited by which models and for which prompts.
  2. Ask ChatGPT and Gemini about your brand sentiment. Understand what the AI sees as your weaknesses and trade-offs.
  3. Improve and defend those weaknesses directly on the cited pages. If the AI thinks your product is "premium in cost" and "less accessible for small teams," add content to your pricing page FAQ that directly addresses those concerns.

The beautiful part: AI-cited pages likely get included in future training data updates. So every citation compounds.

How to Track AEO

AEO tracking is fundamentally different from SEO tracking. You're not watching rankings. You're watching whether AI mentions you.

The leading indicators, in order:

  1. Impressions — your content is being seen by AI agents during research
  2. AI citations — AI is citing your pages as sources
  3. Brand mentions — AI is recommending you by name in responses
  4. Branded search — users are searching for your brand after seeing AI recommendations

Traffic is increasingly a side effect of doing well in AI responses, not the goal. AI answers questions to give users the best response — not to send you traffic. Some citations will drive clicks, but that's not why you're cited.

This is the part that's hardest for leadership to accept. Traffic is declining and unlikely to return. The new ROI is brand visibility in AI responses, and the lagging indicator is branded search volume. If you're measuring AEO success the same way you measure SEO success, you'll conclude it's not working when it actually is.

The Play

The companies that figure this out now have a massive head start. The AI search landscape is where SEO was in 2005 — the rules are still being written, the competition is low, and the queries that matter have zero keyword difficulty because nobody is targeting them yet.

The playbook is deceptively simple: understand what AI thinks about your brand, figure out what it searches for when researching your topics, create entity-rich content for those AI web searches, earn citations, and then improve your brand sentiment on the pages AI is already citing.

Good SEO is still the foundation. But AEO is a new layer on top — one that targets how machines think instead of how humans search. The people who get there first will have their content baked into AI training data for years. The people who wait will be fighting an uphill battle against competitors who already have brand authority in the models.

Why not tackle both?