Here's a quick exercise. Open ChatGPT right now. Ask it to recommend the best product or service in your category. Count how many times your brand shows up.

If the answer is zero, you have an AEO problem.

Answer engine optimization is fundamentally about earning trust, not gaming algorithms. And yet, almost every AEO guide I've read online reads like a repackaged SEO checklist with the word "AI" sprinkled on top. That frustrates me. Because AEO isn't just SEO 2.0. It's a fundamentally different game where your content either becomes the answer an AI engine cites or it doesn't exist at all. There's no position #7 to settle for. No "page two" to languish on. You're either in the answer, or you're invisible.

I've spent the last year building AEO strategies for clients at MaximusLabs, and I'll be honest - there's no universally agreed-upon playbook yet. This field is still being written in real time. But what I've learned, tested, and seen work is what I'm laying out in this guide. Consider this the strategy layer - the why and what - before you dive into the execution-level details in the linked deep dives below.

What Is an AEO Strategy and Why Should You Care? [toc=AEO Strategy Defined]

An AEO (Answer Engine Optimization) strategy is a structured plan for making your content the source that AI search engines - ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude - pull from and cite when answering user questions. Unlike traditional SEO, where the goal is ranking in a list of ten blue links, AEO is about earning citations inside AI-generated responses.

And the stakes are real. As of early 2026, the zero-click rate on Google searches has crossed 65%, meaning most searchers never click through to any website at all. AI Overviews now appear on over 30% of queries, up from just 6.5% in January 2025. Meanwhile, ChatGPT commands roughly 68-80% of the AI chatbot market, with Perplexity growing 370% year-over-year.

💰 The Numbers That Changed My Thinking

Here's the data point that flipped my perspective entirely: Webflow found that traffic from LLMs converts at 6x the rate of traditional Google organic search traffic. They went from 4% of signups coming from LLMs to 8% - and that 8% is dramatically more valuable than the rest.

A broader study of 12 million website visits found AI search traffic converts at 14.2% compared to Google's 2.8% - a 5x difference. AI-sourced customers also have 67% higher lifetime value and 73% lower cancellation rates.

"No one wants a free visitor. Why would you even want a person who is not going to buy from you? I don't know. I don't want namesake visibility."

That's something I say to every client. And those conversion numbers prove my point. AI search doesn't send tire-kickers. It sends people who've already done their research, compared options, and are ready to act. If you want to understand the real ROI math behind this shift, the numbers are staggering.

🔑 The Five Components of a Complete AEO Strategy

A complete AEO strategy has five core components. Each one builds on the last, and skipping any of them leaves gaps that AI engines will notice:

1. Strategic Framework - Your goals, platform priorities, and success metrics
2. Question Research - Discovering the exact questions your buyers ask
3. Query Types - Classifying those questions to determine the right content format
4. Intent Mapping - Connecting questions to buyer journey stages
5. Content Planning - Turning research into a prioritized, executable content calendar

I'll walk through each of these below. But first, let me explain the philosophy that ties them all together - because this is where I think most people get AEO wrong.

Why Revenue-Focused AEO Is the Only AEO Worth Doing [toc=Revenue-Focused AEO]

Most AEO guides will tell you to "optimize for AI visibility." That's like telling someone to "get more traffic." It sounds smart but means nothing without context.

"We are not a typical agency who just increases your clicks and impressions and says that we have done our job. We are not that. We want to have a tangible impact on your business."

At MaximusLabs, we pioneered what I call Revenue-Focused Answer Engine Optimization. The concept is deceptively simple: plan your content so it answers every question your ideal buyer asks across every stage of their buying journey. That's it. That's the strategy.

🎯 The Buyer Journey Is Your Content Roadmap

If you want impact on the business, you need to attract the right person - your Ideal Customer Profile. And you'll only attract the ICP if you deeply understand who they are, what problems they face, and what questions they ask at each stage:

This parallels something Al Ries and Jack Trout wrote about in Positioning decades ago - you win by owning a word, a concept, a space in the buyer's mind. In the AI era, you own that space by literally being cited when the buyer asks the question.

⚠️ Why I Ignore Top-of-Funnel Content (And You Probably Should Too)

Here's my contrarian take, and I might be wrong, but this is my current thinking:

"Today's large language models are well trained on top-of-funnel content. They'll answer the query even without searching the internet. So there's no point in writing top-of-funnel content today."

Think about it. If someone asks ChatGPT "What is SEO?" - it doesn't need to search the web. It already knows. It's been trained on thousands of definitions. Your beautifully crafted "What is SEO?" article is competing against the model's own training data, not just other websites.

What makes complete sense is going super detailed on bottom-of-funnel content - writing everything possible to help the buyer make their decision. These are the queries where AI must search the web because the answers are specific, current, comparative, and product-aware. For B2B SaaS companies especially, this BOFU-first approach is where the real ROI lives.

This is where the real money is. And it's where most AEO guides fall short - they tell you to optimize generically when they should be telling you to optimize strategically for revenue.

Question Research: The Foundation Everything Else Rests On [toc=Question Research]

In SEO, you do keyword research. In AEO, you do question research.

That distinction matters more than it sounds. Keywords are fragments - "AEO strategy," "AI search optimization." Questions are complete expressions of intent - "How do I build an AEO strategy for my B2B SaaS company?"

Ethan Smith of Graphite, whose research I deeply respect, puts it well: just as a single SEO page can target thousands of keywords under one topic, a single AEO-optimized page should target thousands of question variants under one topic.

📊 Where to Find the Questions That Matter

Here's the question research stack I recommend:

• Google's "People Also Ask" boxes - Mine these relentlessly. They're Google telling you exactly what people ask next.
• AnswerThePublic / AlsoAsked - Visualize question clusters around your core topics.
• Google Search Console - Filter for queries phrased as questions. These are gold because they represent real searches that already found you.
• Your sales and support teams - This is the one everyone misses. The questions your prospects ask on sales calls and the tickets your customers submit are the highest-intent questions in existence.
• Reddit and Quora threads - These platforms are among the most-cited sources in AI answers, and they're full of unfiltered buyer questions.

There's a fascinating parallel here to how great product companies do customer discovery. The best founders don't build what they think customers want - they listen obsessively to what customers actually ask. AEO question research is the content marketing equivalent of that discipline.

🚀 The Monday Morning Action Step

Here's what I'd do if I were starting from zero: Take your top 10 paid search keywords. Paste each one into ChatGPT with the prompt "What are the 20 most common questions people ask about [keyword]?" You now have 200 question variants to audit. Cross-reference those with your sales team's most frequently asked questions, and you'll have a question research foundation in a single morning.

I go deep on the complete methodology in the Question Research spoke page.

Query Types: Why Not All Questions Deserve the Same Content [toc=AEO Query Types]

Once you have your questions, you need to classify them. This is where most AEO guides give you the standard SEO taxonomy - informational, navigational, transactional - and call it a day.

That's insufficient for AEO.

🔑 The AEO-Specific Query Classification

Ethan Smith's framework from his Graphite research gives us a more useful lens for AEO:

💡 Why This Classification Changes Everything

For broad head questions, your own website ranking #1 isn't enough anymore. The AI summarizes multiple citations. The winner is the brand mentioned most frequently across all those citations. That means your strategy for head terms is about earned media - getting mentioned on Reddit, in YouTube videos, on affiliate sites, and in industry publications. Understanding how to track your AI search visibility and brand mentions across these platforms becomes essential.

For specific long-tail questions, your website wins. Publishers like NerdWallet can't answer every hyper-specific question about your product - but you can. And the long tail is massive in AI: the average chat query is around 25 words versus 6 words for traditional search.

I break down each query type with full tactical playbooks in the Query Types spoke page.

Intent Mapping: Connecting Questions to the Buyer Journey [toc=Intent Mapping]

Here's where it gets interesting, and where I see the biggest gap in how people approach AEO.

Intent mapping is the bridge between your question research and your content planning. It's the process of taking every question you've identified and connecting it to:

1. The buyer journey stage (Problem Aware -> Solution Aware -> Product Aware -> Competitor Aware -> Decision)
2. The search intent (Informational, Commercial Investigation, Transactional, Navigational)
3. The right content format to satisfy both the user and the AI engine

🎯 Why Intent Mapping Is Non-Negotiable

Without intent mapping, you end up with a disorganized content library that might individually answer questions well but doesn't systematically guide buyers toward a decision. It's similar to how a great retail store designs its floor plan - not randomly, but as a deliberate journey from entrance to checkout.

"If you answer each and every question of the buying journey, then you will be able to attract the right buyer."

This is what I genuinely believe. When you map every question to a buyer stage, you stop creating content randomly and start building a content ecosystem that meets the buyer wherever they are. This is also why strong E-E-A-T signals matter so much - AI engines trust sources that demonstrate consistent, comprehensive expertise across an entire topic.

📊 The Intent Mapping Matrix

At MaximusLabs, we build this matrix for every client before we write a single piece of content. It ensures that every article has a clear purpose in the revenue pipeline - not just a keyword target.

💡 Intent Mapping in Action: A Concrete Example

Let me walk you through how this works with a real question. Suppose your question research surfaces: "Best CRM for a 50-person sales team."

• Buyer Stage? Product Aware. This person already knows CRMs exist. They're evaluating options for their specific situation.
• Intent Type? Commercial Investigation. They're comparing, not just learning.
• Content Format? A detailed comparison listicle or a curated "best of" page that addresses the specific needs of a 50-person team (integrations, pricing tiers, scalability).
• Priority? HIGH. This is a bottom-of-funnel query where the AI must search the web because the answer depends on current pricing, features, and real-world reviews.

Now compare that to: "What is a CRM?" That's Problem/Solution Aware, Informational intent, and LOW priority - because ChatGPT can answer it from training data alone. No need to burn your bandwidth here.

That's the discipline. Every question runs through this filter, and the output is a prioritized content action - not a vague "we should write about CRMs."

The full methodology is in the Intent Mapping spoke page.

Content Planning: Where Strategy Becomes Execution [toc=Content Planning]

Every company has limited bandwidth. If you're a smaller company, this constraint is even sharper. And this is exactly where content planning for AEO differs most from traditional SEO content planning.

"We need to be really strategic about what we're going after. If you go after vague top-of-funnel or middle-of-funnel things, then ultimately you won't get business from it."

✅ The Prioritization Framework

My content planning process follows a clear hierarchy:

1. 🥇 Bottom-of-Funnel First - Competitor comparisons, alternatives pages, product-specific listicles, decision-stage content. These convert. These are what AI engines need to search the web for because the answers are specific, timely, and product-dependent.
2. 🥈 Middle-of-Funnel Second - Solution-aware content like "how to" guides, methodology breakdowns, and framework explainers. Only after BOFU is exhausted.
3. 🥉 Top-of-Funnel Last (If Ever) - Definitional content, awareness-stage education. The AI already knows this. Your marginal return here is close to zero for most businesses.

⏰ The Bandwidth Reality

If I'm being candid, most companies I talk to can realistically produce 5-10 high-quality pieces of content per month. That's it. So the question isn't "what should we write about?" - it's "what 5-10 pieces will have the highest revenue impact?"

Start with your AEO implementation checklist and identify the 5-10 BOFU questions where your product has the strongest answer. Write those first. Then look at what's already getting cited in your space and build from there.

🔑 Hub-and-Spoke Architecture for Topical Authority

A hub-and-spoke content architecture - where a central strategy page connects to detailed pages on each subtopic - signals deep expertise to both human readers and AI systems. This is the model I recommend for every SaaS startup approaching GEO seriously.

Research suggests that content hub architectures built around topic clusters receive significantly more AI citations than standalone articles because they demonstrate topical depth and interconnectedness - exactly what LLMs look for when evaluating source authority.

Your content plan should map to this architecture: each spoke page targets a specific cluster of questions, and the hub page ties them together into a coherent strategy narrative. Pair this with solid technical GEO implementation and proper schema markup, and you're building the kind of trust infrastructure that AI engines reward.

I detail the full content planning workflow - including prioritization matrices, editorial calendars, and resource allocation models - in the Content Planning spoke page.

What I'm Thinking About Next [toc=Future Predictions]

My prediction - and I could be completely wrong - is that within two years, the companies that built systematic AEO strategies will have compounding citation advantages that are nearly impossible for latecomers to overcome. It's similar to how early SEO adopters in 2008-2010 built domain authority moats that still pay dividends today.

The difference is that AEO rewards trust, not just authority. And trust is built through genuine expertise, consistent helpfulness, and the kind of deep buyer-journey coverage that most companies are too lazy or too generic to build.

If you're reading this and feeling overwhelmed, start small. Pick your five most important buyer-stage questions. Answer them better than anyone else on the internet. Optimize for clarity, structure, and depth. Then expand from there.

That's exactly what we do at MaximusLabs - we start with your ICP, walk through their buyer journey, and build outward from the questions that directly impact your revenue. Not vanity metrics. Not "brand awareness." Revenue.

The spoke pages below are your next step. Pick the one that matches where you are in your AEO journey:

• AEO Strategy Framework ->
• Question Research ->
• Query Types ->
• Intent Mapping ->
• Content Planning ->