If you care about organic growth right now, you are not actually fighting for “position 1 on Google”.

You are fighting to be the sentence, table, or bullet that an AI system decides to copy into its answer.

That is Answer Engine Optimization.

In this guide I will walk through how I think about AEO, what the data is showing, and how to build a real strategy instead of chasing yet another acronym. It is written for people who already know SEO and want a serious, practical operating manual for AI search.

1. What is Answer Engine Optimization, really?

Let’s start with a tight definition in plain language.

Answer Engine Optimization (AEO) is the practice of making your brand and content:

1. Discoverable by AI systems that answer questions.
2. Easy to parse into reusable chunks.
3. Trustworthy enough to be selected and cited in the final answer.

“Answer engines” here means experiences like:

• Google AI Overviews & AI Mode (powered by Gemini)
• ChatGPT Search
• Perplexity
• Microsoft Copilot / Copilot Search
• Claude with web search
• Grok and other X-style live-answer systems

In classic SEO, the unit you optimize is a page ranking in a list of blue links.

In AEO, the unit you optimize is a fact, explanation, or snippet being pulled into an answer that may or may not send you a click. That matches how emerging definitions describe AEO / GEO / LLM SEO: optimizing content for inclusion in AI-generated answers, not just search results.

Different labels are floating around:

• AEO: Answer Engine Optimization
• GEO: Generative Engine Optimization
• LLM SEO / LLMO: Optimization for LLM-based surfaces across search, chat, and agents

The naming is messy. The principles are converging.

For this piece, I will use AEO as the umbrella term and treat GEO / LLMO as variations on the same basic problem: “How do I make sure AI systems describe my brand correctly and use my content when they answer?”

2. Why AEO matters now (with real numbers)

AEO is not hype at this point, it is a measurement problem.

A few numbers to ground it:

• AI referrals to the top 1,000 websites hit 1.13 billion visits in June 2025, up 357% year over year, driven by experiences like Copilot and other Bing-powered AI surfaces.
• Bain’s 2025 research found about 80% of consumers now rely on AI-written or zero-click answers for at least 40% of their searches, cutting organic web traffic by 15–25%.
• Google says AI Overviews have passed 1.5 billion users, and AI Mode is rolling out as a conversational layer on top of search.
• ChatGPT is now a top-tier web property, with Similarweb estimating around 5.9–6.2 billion visits per month in late 2025.
• Perplexity is already in the ~170–220 million monthly visit range and growing, positioning itself explicitly as an “answer engine”.

On the other side of that growth:

• Multiple studies now show serious traffic drops for publishers when Google shows AI Overviews, including reports of 50–80% click loss when an AI summary sits above the organic results.
  
• Similarweb has found that visits referred by ChatGPT convert at 11.4%, compared to 5.3% from classic organic search for ecommerce.
  
  

So the story is not “SEO is dead”. It is:

• Traffic is shifting into AI layers where visibility is harder to measure.
  
• Those AI referrals are still small in volume but high in intent and conversion.
  
• The real risk is not losing your Google ranking; it is becoming invisible inside the answers people actually read.
  
  

3. AEO vs SEO: same foundations, different surface

SEO is still the foundation. AEO sits on top of it.

Here is the simplest way to think about the differences.

AEO vs SEO comparison

Important mindset shift:

• SEO asks: “How do I get this page to rank for this query?”
  
• AEO asks: “When someone asks this system a question, what does it say, and what sources does it lean on?”
  

You still need fast, crawlable pages with real authority. But that is the entry ticket, not the whole game.

4. How answer engines actually build answers

Most of the tactical debates in AEO come from using the wrong mental model.

Answer engines do not read your article top to bottom and decide if you “deserve” a rank. They:

1. Interpret the query or prompt (intent, entities, constraints).
   
2. Retrieve candidate content from indexes like Google, Bing, or their own crawls.
   
3. Break pages into chunks and score those chunks for relevance, clarity, and trust. Microsoft’s team literally describes this parsing and chunk ranking process in their AI Search content guidance.
   
4. Synthesize an answer in natural language that tries to be correct, concise, and on-brand for the assistant.
   
5. Decide which URLs to show as citations or source cards and how visually prominent they should be.
   
6. Optionally trigger extra actions: product modules, maps, shopping recommendations, or internal tools.
   
   

Goodie’s 2025 AEO Periodic Table, based on 2.2 million live prompts across major AI platforms, backs this up: models are assembling answers from many small pieces of content, weighting factors like content depth, trust signals, and citation networks, not just “who is rank 1”.

One line from that work sums it up nicely: LLMs do not rank pages; they assemble answers. Your job is to be the easiest, most credible piece to fetch, verify, and stitch in.

That is exactly what AEO optimizes for.

5. AEO strategy: a practical framework

Let’s walk through a strategy you can actually run with a lean team.

I think about AEO in six loops:

1. Map your surfaces and engines
2. Audit what AI already says about you
3. Do prompt and topic research
4. Build AI-ready content
5. Fix technical AEO
6. Measure >> learn loop

5.1 Map your surfaces and engines

You cannot optimize for “AI” in the abstract.

Start by listing the actual answer engines that matter for your business:

• Google AI Overviews + AI Mode (Gemini)
  
• ChatGPT Search (and shopping if you sell products)
  
• Perplexity (especially for research-heavy categories)
  
• Bing / Copilot Search
  
• Claude web search for B2B and knowledge work
  
• Grok if your audience lives on X
  

For each, quickly sanity check:

• Does my audience actually use this?
• Does it show sources and links?
• Is it pulling from the open web, or mostly internal data?

Prioritize 2–3 engines first instead of spreading thin.

5.2 Audit what AI already says about you

Before publishing anything new, you need a clear picture of your current “AI reputation”.

There are two ways to do this:

1. Manual sweeps
   
   • Ask each engine branded questions:
     
     • “Who is [brand]?”
     • “Is [brand] good for [category]?”
     • “Best tools for [use case]” and see if you appear.
   • Log the exact answers, sources, and any obvious errors or outdated claims.
     
2. Dedicated AEO / AI visibility tools
   Tools like Goodie, Ahrefs Brand Radar, Profound, and Peec aggregate thousands of prompts across ChatGPT, Gemini, Perplexity, Claude, and Copilot to show how often you appear, where you rank inside answers, and what the engines actually say.
   
   

You are looking for three things:

• Misstatements or missing facts about your brand
• Topics where competitors appear and you do not
• The specific pages and domains the engines love to cite for your category
  
  

That gives you your “gap list”.

5.3 Prompt and topic research (AEO-flavored)

Keyword research still matters, but AEO cares more about the questions and prompts your audience uses.

Practically:

• Mine “People Also Ask”, Reddit, community forums, and your own support tickets for real questions.
• Use conversational keyword tools, but translate head terms into prompts.
• Track AI-triggering queries with tools like the Google AI Overview Impact Analysis extension, Semrush AIO modules, or similar, so you know where AI Overviews already appear.
  

For each cluster, write down:

• Primary question
• Variants people actually type or say
• Intent (research, comparison, “what should I do”, “which tool is best”, etc)
• Engines where this query tends to trigger AI answers

This becomes your AEO content backlog.

5.4 Build AI-ready content (without ruining UX)

AI search systems like Copilot and Microsoft’s ads team have been explicit about what they prefer: clear titles, aligned H1s, descriptive headings, Q&A blocks, lists, and tables that break content into modular chunks.

Here is the pattern I recommend for AEO content:

1. Bottom line up front
   
   • Answer the core question directly in the first 40–60 words.
   • Make it snippable: a sentence that still makes sense if copy-pasted alone.
     
2. Headings that behave like API endpoints
   
   • Use descriptive H2 / H3 headings that mirror real questions.
   • Example: “What is answer engine optimization?” beats “Overview”.
     
3. Q&A blocks where it feels natural
   
   • Insert explicit Q: / A: sections for highly asked questions.
   • Do not overdo it; you are still writing for humans.
     
4. Tables and lists for comparisons, steps, and specs
   
   • Specifications, pros/cons, versus pages, checklists all belong in table or list form.
   • Microsoft, Google, and others hint that these are easier to quote and re-use in AI answers.
     
5. Semantic clarity over marketing fluff
   
   • Replace vague claims like “next-gen” with measurable facts.
   • Include units, ranges, and constraints that AI systems can latch onto.
     
6. Information gain
   
   • Say something the model will not see on every other blog. Original research, benchmarks, teardown screenshots, or data from your own customer base all act as visibility multipliers. Goodie’s AEO Periodic Table and similar studies consistently show content depth and unique data as top visibility factors.
     

You do not need weird content chunk hacks. You just need pages that are structured cleanly enough that a machine can understand where one idea ends and another begins.

5.5 Technical AEO: give the crawlers a fair shot

All the classic SEO hygiene still applies:

• Fast pages, good Core Web Vitals
  
• Mobile-friendly templates
  
• Logical internal linking
  
• Clean HTML, no important content hidden in images or JS-only widgets
  
  

For AEO, add three more layers.

1. Schema and structured data
   Use JSON-LD schema to spell out what your content is:
   
   • Article, BlogPosting for editorial content
   • FAQPage for Q&A blocks
   • HowTo for step-by-step guides
   • Product, Review, and Offer for ecommerce
   • LocalBusiness for locations
   • Speakable and similar where available for voice-friendly answers
     
     
2. Schema has been a ranking and rich result signal for years, and even if every AI surface does not fully use it yet, it is a cheap way to convert content into machine-readable facts.
   
   
3. AI crawler access and control
   
   • Check and tune your robots.txt for AI crawlers like GPTBot, ClaudeBot, and PerplexityBot, which most labs say they obey.
     
   • Decide where you want to be ingested and where you do not. Some publishers now use robots plus emerging licensing standards like RSL to negotiate payment while still allowing access.
     
   • If it is easy for your stack, experiment with llms.txt / llms.txt style files that point AI crawlers to clean exports and canonical documentation, with the caveat that support is still early and uneven.
     
     
4. Site architecture for chunking
   
   • Use a stable URL structure, especially for guides that AI will cite long term.
     
   • Keep important answers in HTML, not buried in PDFs or hidden tabs. Microsoft’s AI Search guidance explicitly warns that hidden or tabbed content is much harder for AI to use.

5.6 Authority and distribution

Answer engines are very sensitive to authority and co-mentions.

You want to:

• Earn links and mentions from domains that already get cited in AI answers for your topics. Tools like Brand Radar, Goodie, Profound, and various AEO audits report “top cited domains” for your niche so you know which publications matter.
  
• Seed your best explanations as guest posts, talks, or reports on those domains so LLMs repeatedly see your brand name next to trusted entities.
  
• Keep your NAP, descriptions, and value props consistent everywhere.

6. How to optimize for specific answer engines

Each major answer engine has its own flavor. You do not need a completely different playbook for each, but small tweaks pay off.

Engine-by-engine AEO cheat sheet

You do not need to memorize every quirk. The recurring theme is:

• Clear, modular, factual content
  
• Strong off-site authority
  
• Clean technical implementation so crawlers and grounding systems can see and reuse your work
  

7. Measurement, tooling, and AEO KPIs

You cannot manage what you cannot see. With AEO this is the real pain.

You have three levels of measurement:

7.1 Surface-level: where do I show up?

Tools and workflows here:

• AI Overview Impact Analysis extension to see which of your target queries trigger Overviews and whether your URLs show up.
  
• Manual logging of how often your brand appears when you ask common prompts in ChatGPT, Perplexity, Claude, Copilot, and Gemini.
  
• Dedicated AEO platforms such as:
  
  • Goodie (full disclosure: the platform I am closest to conceptually), which tracks visibility, sentiment, and competitive share across major answer engines.
  • Ahrefs Brand Radar, which aggregates prompts and AI responses to show your AI share of voice, cited pages, and competitor comparisons.
  • Profound, Peec, and similar GEO / AEO tools that specialize in AI search analytics.
    

Key metrics:

• AI visibility rate: % of tested prompts where your brand appears at all
• Share of answer: how often you are cited versus key competitors
• Answer position and prominence: are you in the first 2–3 sources or buried in a secondary block?
• Sentiment and framing: how your brand is described inside answers

7.2 Traffic-level: what do these answers actually send?

Classic analytics tools still work here:

• Track referrals from chat.openai.com / chatgpt.com, perplexity.ai, claude.ai, bing.com, and Google AI surfaces notated with special parameters.
• Segment those visits into their own acquisition channel so they do not get lumped into “organic search” or “direct”.
  

Remember: volume may be small today, but we already know AI referrals tend to convert better on average, at least in ecommerce.

7.3 Business-level: what moves revenue and pipeline?

Tie AEO work into:

• Lead and signup quality from AI referrals
• Assisted conversions where AI referrals appear early in the journey
• Category-level share of mind: are you being recommended in “best X for Y” answers as often as the rest of your competitive set?
  

One more thing: embrace the volatility. AEO studies consistently show that 40–60% of citations change month to month across AI Overviews, ChatGPT, Copilot, and Perplexity.

Your job is to increase the probability that these engines pick you, not to lock in a permanent “rank”.

8. The future of AEO

AEO is still early, but a few trends are pretty clear.

8.1 Multimodal answers

Answer engines are rapidly moving beyond text:

• Google is already experimenting with visual and video overviews in tools like NotebookLM and AI Overviews.
  
• ChatGPT, Gemini, Claude, and Grok are all leaning into images and charts in answers.

That means you will eventually need AEO for video chapters, diagrams, product photos, and even interactive elements, not just text.

8.2 Personalization and agents

As personal AI agents and browser-like experiences (ChatGPT Search, AI Mode, Perplexity Pro search, etc) become the default discovery layer, answers will adapt to:

• User history
• Workspace context (email, docs, CRM)
• Team-wide preferences

Your optimization work will shift from “one SERP for everyone” to “am I part of the shortlist this agent trusts when it makes a recommendation for this user”.

8.3 Standards and governance

We are already seeing:

• Growing use of robots.txt to manage AI training access
• Emerging standards such as llms.txt and RSL that try to govern how AI companies use and pay for content
• Stronger scrutiny on AI citation quality and hallucinations from regulators, courts, and publishers
  

For brands, that means AEO will live at the intersection of SEO, legal, and partnerships.

8.4 The boring truth

The big unlock is not a trick.

Everything serious we are seeing in studies like Goodie’s AEO Periodic Table, Ahrefs’ Brand Radar data, Bain’s surveys, and Similarweb’s reports points to the same pattern.

• Technical hygiene
• Strong, structured, truly helpful content
• Real authority and mentions in your category

It just needs to be tuned to a world where your reward is a sentence inside an AI answer instead of a blue link.

9. FAQs (the short AEO playbook)

Is AEO going to replace SEO?‍

No. AEO depends on SEO. If your site is slow, uncrawlable, and lacks authority, you will not get AI citations at scale. The difference is what you track and how you structure content.

How long does AEO take to work?‍

For established brands with solid SEO, you can see movement in weeks once you fix obvious brand gaps and publish AI-friendly content on existing strong URLs. For newer brands without authority, think 12–18 months of consistent publishing and link earning before you see reliable visibility in AI answers.

Can I “force” AI systems to say specific things about my brand?‍

You can influence, not control. The highest leverage moves are:

• Publishing clear, consistent facts about your brand across your own site and major third-party profiles
  
• Correcting misinformation where you find it
  
• Giving AI models an abundant supply of accurate, structured, high-signal content to pull from
  

What are the top 5 AEO tactics I should do this quarter?

1. Run an AI visibility audit: what do ChatGPT, Gemini, Perplexity, Claude, Copilot, and Grok say about you today.
   
2. Fix brand basics: homepage, about page, and key product pages with clear definitions, FAQs, and schema.
   
3. Publish 2–3 “answer pages” for your most important questions, structured with BLUF summaries, Q&A sections, and tables.
   
4. Start tracking AI Overviews and AI referrals separately in your analytics.
   
5. Pick one AEO monitoring platform and use it monthly to guide which topics and pages you improve next.
   

If you treat AEO as an ongoing operating system for search, rather than a side experiment, you will be ahead of most of the market that is still arguing about what acronym to use.