Here's a number that should get your attention: by early 2026, an estimated 40% of informational searches now involve an AI-generated response — whether that's Google's AI Overviews, ChatGPT with browsing, Perplexity, or one of the dozen other AI search tools that have launched in the past year. The search landscape hasn't just shifted; it's fractured into something fundamentally different from what existed even two years ago.

I've been obsessing over this shift for months. As someone who works in digital marketing in San Diego, my livelihood depends on understanding where traffic comes from and how to capture it. And what I've realized is that we need a new framework — not a replacement for SEO, but a complement to it. I've been calling it AEO: AI Engine Optimization. And before you roll your eyes at another acronym, let me show you why this matters and what you can actually do about it.

The AI Search Landscape in 2026

Let's map out what we're actually dealing with, because "AI search" isn't one thing — it's several very different systems with different behaviors:

Google AI Overviews

Google's AI Overviews (the evolution of what started as Search Generative Experience) now appear on roughly 30% of search queries. They pull information from multiple sources and generate a synthesized answer at the top of the results page. The key insight: AI Overviews still cite sources with clickable links. If your content is cited, you get traffic — sometimes more than you'd get from a traditional blue link, because the AI Overview positions your site as an authoritative source.

ChatGPT with Browsing

ChatGPT's browsing mode actively searches the web to answer questions. When it finds and uses your content, it typically cites the source with a link. ChatGPT tends to prefer recent, comprehensive, well-structured content from sites with clear expertise signals. It's less influenced by traditional ranking factors and more influenced by content quality and clarity.

Perplexity

Perplexity has carved out a serious niche as a research-focused AI search engine. It's transparent about its sources, always providing numbered citations. Perplexity indexes aggressively and seems to favor content that directly answers specific questions with evidence and data. It's become my go-to example when explaining AEO to clients because the citation mechanism is so visible.

Other AI Tools

There's also Anthropic's Claude (which can access web content), Microsoft Copilot (integrated with Bing), and various vertical-specific AI tools. Each has its own source selection logic, but the patterns I'll describe below apply broadly.

How AI Models Choose Sources to Cite

This is the million-dollar question, and I've spent considerable time studying the patterns. While each AI system is different, there are clear commonalities in what makes content "citable" by AI:

1. Clear, Factual, Direct Answers

AI models love content that directly answers questions with specific, verifiable information. Vague, hedging, wishy-washy content gets passed over. If your article says "there are several ways to approach this" without actually enumerating them clearly, an AI model will find a source that does enumerate them.

The pattern I've noticed: content that uses the journalistic "inverted pyramid" structure — key information first, details after — gets cited significantly more often than content that buries the answer.

2. Original Data and Unique Insights

AI models are essentially aggregators — they synthesize information from multiple sources. If your content just rehashes what everyone else says, the AI has no reason to cite you specifically. But if you have original data, unique case studies, personal experience, or a genuinely novel perspective, you become a valuable source that the AI needs to cite to give a complete answer.

3. Structured, Well-Organized Content

Content with clear headings, logical structure, and well-organized sections is significantly easier for AI to parse and cite. I've tested this directly: taking the same information and presenting it in two formats — one as a wall of text, one with clear H2/H3 headings and bullet points — the structured version gets cited by AI tools at roughly 3x the rate.

4. Demonstrated Expertise (E-E-A-T Signals)

AI models — especially Google's — evaluate the credibility of sources before citing them. Author bios, about pages, credentials, published work history, and the overall authority of the domain all factor in. This is deeply connected to entity SEO — the stronger your entity signals, the more likely AI systems are to trust and cite your content.

5. Recency

For evolving topics, AI models strongly prefer recent content. A comprehensive guide from 2024 will lose to a comprehensive guide from 2026 on any topic where things have changed. Keep your key content updated, and include clear date signals (publication date, "last updated" notes).

Content Structure That Gets Picked Up by LLMs

Based on my testing and observation, here's a content structure template that maximizes your chances of AI citation:

The AEO-Optimized Article Structure

  1. Lead with a clear thesis or key finding. The first paragraph should contain the most important information someone would want from this article. AI models often pull from the first few paragraphs.
  2. Use descriptive H2 headings that match likely queries. Instead of "Background" use "How [Topic] Works in 2026." AI models use headings as navigation to find relevant sections.
  3. Include definition blocks for key concepts. When you define a term or concept, make it crystal clear. "AEO (AI Engine Optimization) is the practice of optimizing content to be discovered, cited, and linked by AI-powered search tools." That's citable. A paragraph-long rambling definition isn't.
  4. Use lists and structured formats for key takeaways. Numbered lists, comparison tables, pros/cons — these are easy for AI to extract and present in answers.
  5. Provide specific examples and data. "Conversion rates increased by 23%" is citable. "Conversion rates improved significantly" is not.
  6. End sections with clear summary sentences. AI models often pull summary statements as key points in their generated answers.

Actionable AEO Tactics

Enough theory. Here's what you should actually do:

Tactic 1: Create "Answer Target" Content

Identify questions that people are asking AI tools about your industry. You can find these by using Perplexity and noting which queries generate responses that cite few or low-quality sources — that's your opportunity. Create content that is the best possible answer to that question.

Example: If you're a real estate agent in San Diego, search Perplexity for "best neighborhoods in San Diego for young professionals 2026." Look at what gets cited. Can you create something better? Something with more specific data, more personal insight, more neighborhood-by-neighborhood detail? Do that.

Tactic 2: Implement Comprehensive Structured Data

AI models use structured data to understand and trust content. At minimum, every article should have:

Tactic 3: Build a "Citable Quotes" Section

This is a technique I stumbled onto that works remarkably well. At the end of in-depth articles, include a section called "Key Takeaways" or "Summary" with 5-7 concise, quotable statements. AI models frequently pull from these sections because they're pre-formatted as standalone, citable facts.

Tactic 4: Create Comparison and "vs" Content

AI search tools get a massive volume of comparison queries: "X vs Y," "best tools for Z," "alternatives to W." This type of content is incredibly well-suited for AI citation because it requires specific, structured information that AI models need sources for. If you have genuine expertise comparing tools, services, or approaches in your industry, create definitive comparison content.

Tactic 5: Maintain a Living Knowledge Base

Rather than just blogging, consider maintaining a knowledge base or resource hub (like a curated resources page) that you update regularly. AI models value freshness and comprehensiveness. A living resource that gets updated monthly is more likely to be cited than a static blog post from last year.

Tactic 6: Optimize for "People Also Ask" and Related Queries

Google's "People Also Ask" boxes are generated using similar logic to AI Overviews. Content that consistently earns PAA features is well-positioned for AI citation. Structure your content to directly answer these questions, and you'll often find yourself cited in both PAA and AI Overviews.

Measuring AEO Success

One of the biggest challenges with AEO is measurement. Unlike traditional SEO where you can track rankings and clicks in Search Console, AI citation tracking is still primitive. Here's what I do:

The Future of AEO

I think we're at the very beginning of this shift. Within the next year or two, I expect to see dedicated AEO analytics tools emerge, more sophisticated AI citation tracking, and potentially even "AI search console" type products from Google and Microsoft.

The businesses and creators who start thinking about AI optimization now will have a significant head start. The fundamentals are familiar — create great content, structure it well, demonstrate expertise, keep it updated. But the tactics and the measurement are new, and they reward a different kind of optimization than traditional SEO.

The way I think about it: traditional SEO is about convincing a ranking algorithm that your page deserves to be at the top of a list. AEO is about convincing an AI model that your content deserves to be cited as a source. Both matter. Both will coexist. But if you're only doing the first one in 2026, you're leaving a growing share of traffic on the table.

For the entity-building foundation that makes AEO work, check out my complete guide to entity SEO. And if you're wondering how all of this applies specifically to the tech ecosystem I work in, I wrote about San Diego's growing tech scene — there's an interesting intersection between the local AI startup boom and how these search tools are reshaping digital marketing here.