AEO: How to Make AI Recommend Your Product
The playbook for getting mentioned by ChatGPT, Perplexity, and Google AI — because if AI doesn’t know you exist, neither do your buyers
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Dear GTM Strategist,
Here’s a question that should keep every B2B founder up at night: What does AI say when someone asks for the best tool in your category?
The new “homepage” for your buyers is no longer your website. It’s a ChatGPT prompt. And if you’re not in the answer, you don’t exist.
ChatGPT, Perplexity, and Google AI Overviews are the new front doors for buyer research. According to Forrester, 94% of B2B buyers now use AI during their purchasing process — and most companies have no idea what’s being said about them (or worse: nothing is being said at all).
Prospects visit your website later in the buying process, but are better informed.
So it’s no surprise that 51% of GTM practitioners said they plan to increase their investment in AI discovery and Answer Engine Optimization (AEO) in 2026 (source) — the top channel bet by a wide margin.
But the playbooks are still being written in real time. It’s part gold rush, part black box, and part “nobody actually knows what they’re doing yet.”
So I rolled up my sleeves and did the research on how to do AEO in 2026 - for B2B products. This article gives you:
Why AEO is the most urgent GTM priority right now — the data that makes this undeniable
What AEO actually is (and what it’s not) — cut through the naming chaos and snake oil
A 6-step playbook to audit and improve your AI visibility starting this week
Real case studies from companies getting 10-25% of signups from AI discovery
The tools and stack to monitor your “Share of Model” and track AI-referred pipeline
Let’s get into it.
The shift is no longer theoretical. It is measurable, accelerating, and directly impacting pipeline.
AI search is massive — and growing fast. ChatGPT now has 800 million weekly active users processing 2.5 billion prompts per day. Google AI Overviews appear in 13% of all searches — double since January. Gartner predicts that by end of the year, 25% of organic search traffic will shift to AI chatbots and virtual assistants.
The conversion quality is insane. This is what really caught my attention. Webflow reported that their ChatGPT traffic converts at 24% — 6x higher than Google. Ahrefs found that AI search accounts for just 0.5% of their visitors but 12.1% of signups — a 23x higher conversion rate. Surfer SEO sees roughly 25% of new customers coming from AI assistants.
AI-referred traffic is a fraction of the volume but disproportionately high-intent. These buyers show up pre-sold because the AI already told them you’re the best option.
And yet almost everyone still feels in the dark when it comes to optimizing for AI search. A 10Fold study of 400 senior marketing execs found that only 11% of B2B firms have the majority of their content ready for AI discovery. There’s a massive gap between awareness and execution — and that gap is your window.
What AEO Actually Is
AEO (Answer Engine Optimization) is about making your product the answer when buyers ask AI for recommendations. Not ranking on a results page. Not getting clicks. It’s about being cited, summarized, and recommended inside a conversation.
The naming situation is a mess - I’m sticking with AEO, but it’s also called GEO (really? 🌍), LLMO, AIO (all in one?), SXO, AI SEO… But the name matters less than the concept.
Here’s the mental model shift that matters:
In SEO, you compete for rankings. In AEO, you compete for mentions. The new metric isn’t “Position 1” — it’s “Share of Model,” which measures how often your brand is the recommended answer across AI platforms. Webflow tracks this obsessively and has grown their AI category share from 1.2% to roughly 60%.
What AEO is not:
It’s not “new SEO.” AEO extends SEO — it doesn’t replace it. Late-funnel SEO is the foundation. If you don’t rank for “[your category] for [use case],” you probably won’t show up in AI answers either. AI models pull from the same sources that rank in traditional search.
It’s not gaming LLMs. There’s a lot of snake oil out there — prompt injection tricks, hidden text, keyword stuffing for AI. This is short-term thinking at best and will get you flagged at worst.
It’s not just a content project. This is what Kyle Poyar warns about most: “AEO isn’t a content thing or an SEO experiment. It’s a GTM capability.” It touches product marketing (how your product is described), content (what you publish), and brand (what third parties say about you).
It’s not about publishing more. Volume without structure and authority doesn’t improve AI visibility.
The AEO Playbook: How to Get AI to Recommend You
Here’s what I’d recommend based on what the fastest-moving companies are actually doing — not theory, but deployed and producing results.
Step 1: Audit Your AI Visibility
Before you optimize anything, you need to know where you stand. This takes 30 minutes and will probably surprise you.
Run the prompts your buyers would run. Open ChatGPT, Perplexity, Gemini, and Google AI Mode. Type the questions your ICP actually asks: “What’s the best [your category] tool?” “Compare [you] vs [competitor].” “Best [your category] for [specific use case].” Do this for 15-25 variations across the buying funnel.
Track three things for each prompt:
Are you mentioned at all?
Are you recommended or just listed?
How are you described? Is it accurate? Is it how you’d want to be positioned?
You might be the #1 result on Google and completely invisible in ChatGPT. These are different systems with different signals. One company can dominate in ChatGPT and be absent from Perplexity — each platform has its own retrieval logic and citation patterns.
Tools that can help: HubSpot’s Search Grader (free baseline), Profound, Xfunnel, Ahrefs Brand Radar. But for the start, manual testing will do. There’s no substitute for seeing exactly what AI says about you right now.
Step 2: Build Your Prompt Library
Once you’ve done the initial audit, systematize it. Identify the 15-25 core prompts that define your category across the buying funnel:
Top of funnel: “What is [category]?” “How does [category] work?” “Do I need a [category] tool?”
Mid-funnel: “Compare [you] vs [competitor].” “Best [category] for [industry/use case].” “What are the pros and cons of [you]?”
Bottom of funnel: “[You] pricing.” “[You] vs [competitor] for enterprise.” “Is [you] worth it?”
Map yourself and each competitor against every prompt. This becomes your AEO scoreboard — your “Share of Model” tracker. Update it monthly because AI outputs shift with model updates and new content entering the training data.
This is not unlike building an ICP or a messaging framework — you’re defining the battleground. Except instead of a sales pitch, you’re defining the conversations AI is having about your market without you in the room.
Choosing the right prompts: a view from the practice
Not all AI searches are equal, says José Velez, co-founder and CEO of Reach, an AI search research lab running R&D on how AI search engines actually decide what to recommend. “The ones that matter are the high-intent queries across your buyer journey: problems your ICP is searching for solutions to, the specific solutions and categories they're evaluating, and the comparisons they're running between specific vendors. Volume metrics from traditional SEO don't apply here; what matters is intent and proximity to a buying decision.”
So after preparing your list of core prompts, you should reverse-engineer what’s actually in the answers. “For each target prompt, study what the models are citing in that case. Is it a listicle? Owned content from vendors? Review sites? What categories of companies are included? What comparison factors matter? This tells you exactly what your content must cover, what format it should take, and whether you can realistically influence the answer.”
Step 3: Win at Late-Funnel SEO First
Here’s the thing that should relieve a lot of anxiety: you don’t need to start from scratch. Late-funnel SEO is the foundation for great AEO.
AI models rely on live web search (through RAG — retrieval-augmented generation) to construct their answers. They pull from the same content that ranks in traditional search. If your best comparison pages, use case pages, and product pages rank well for high-intent keywords, you’re already ahead of most competitors for AEO too.
What to prioritize:
Comparison pages: “[You] vs [Competitor]” — these are gold for both SEO and AEO
Use case pages: “Best [category] for [specific industry/team/problem]” with specific, citable details
Product pages with clear definitions: Open with what you are, not marketing fluff. AI pulls the first clear definition it finds
ROI/pricing pages: Detailed, transparent, and structured — these directly feed AI responses about your product
Technical docs and integration guides. This one is wildly underrated.
Talal Syed leads AEO and SEO strategy at GrowthX. They ran a study across 1 million AI responses and found something that should change how teams prioritize content:
“Docs pages - like technical docs and integration guides - were 8.6x more likely to get cited than other pages on a site when buyers were actively evaluating products. The long tail has exploded with AI search, and these engines are hungry for high-quality content that goes deep, is well organized, and answers the specific questions people have about products. The brands filling that gap on their own sites are the ones showing up in AI.”
If you’re a B2B SaaS company and your docs are an afterthought, you’re leaving citations (and pipeline) on the table.
For product companies, José Velez consistently sees the same category of underproduced, high-intent content driving outsized results: competitor pricing guides, competitor reviews tied to specific use cases, jobs-to-be-done alternatives (”How to embed tables in Notion” - with your product positioned as the faster option), and “How to cancel [Competitor]” articles that intercept buyers already looking to switch.
“These capture buyers at the exact moment they’re evaluating alternatives, which is exactly what AI models surface as answers, but most teams don’t produce them because they’re not intuitive. Disclaimer: don’t apply this blindly without first validating there’s real search demand in your space,” says José.
Step 4: Structure Content for AI Interpretability
AI systems don’t read content the way humans do. They extract, chunk, and reassemble. Your content needs to be built for this.
Freshness is critical. Research from AirOps found that 83% of AI citations come from pages updated within the past 12 months, with more than 60% refreshed within six months. If your best content is a year old and untouched, it’s losing AI visibility every quarter.
Practical formatting changes that matter:
Lead with direct, definitional answers. Don’t warm up for three paragraphs before defining what your product does. AI pulls the first clean answer it finds. Start every key page with a single, citable sentence: “[Product] is a [category] that does [specific value].”
Use clear, semantic headings that mirror the questions buyers ask. Not “Our Approach” but “How [Product] Handles [Specific Problem].”
Modular content structure. Each section should stand alone when extracted from the page. AI systems break content into passages, not pages.
Add FAQ sections with schema markup. These are directly extractable by AI. Use real customer questions, not marketing-speak.
Implement structured data (Schema.org) — Product, FAQ, HowTo, Organization schemas make your content machine-readable. This isn’t optional anymore.
Case Study: Webflow’s FAQ + Schema Markup Automation
Josh Grant, previously VP of Growth at Webflow, shared how a focused experiment on just six core feature pages (Design, CMS, SEO, Shared Libraries, Interactions, Hosting) drove outsized AEO results - with no content overhaul and no agency.
His team built an AI workflow in AirOps that used Perplexity to deep-research Google’s “People Also Ask” results, Reddit threads, and niche forums to surface the exact questions buyers were actually asking. From there, the workflow identified gaps in existing FAQ content, generated high-intent Q&A pairs, wrote on-brand answers, and auto-structured everything into clean schema markup.
The results: +331 new AI citations (57% of all new citations across Webflow.com that period) and +149K SEO impressions, a 24% lift. As Josh put it: “AI didn’t change the game here. It just raised the bar for how precisely you can answer the questions that already exist.”
Advanced Move: Make Your Product Machine-Readable
AEO in 2026 is evolving beyond optimizing for crawlers toward declaring structured intent for AI agents. Dima Durah, Product Growth at Toloka.ai, argues the highest leverage move is to publish a machine-readable “source of truth” about your product - pricing, capabilities, and constraints - structured, explicit, and owned by you.
For example, in addition to a typical pricing page written for humans, expose a machine-readable version alongside it: a /pricing.json or /pricing.md that an agent can reliably parse.
“Agents don’t want pages, they want structured truth they can act on. The teams that win are the ones that make their context explicit, machine-readable, and impossible to misinterpret.” - Dima Durah, Product Growth, Toloka.ai
This is forward-looking but increasingly practical. If you don’t define your context for AI agents, they will infer it - and they might get it wrong.
Dima previously shared her workflow for creating an AI knowledge graph for AEO. Find the exact prompts and instructions in this GTM Strategist article.
Step 5: Build Citation Signals and Authority Off-Site
Here’s what a lot of teams miss: AI answers are heavily influenced by what other sources say about you. It’s not just about your website — it’s about your entire digital footprint.
Third-party review platforms matter enormously. G2, Capterra, and TrustRadius are among the most-cited sources when AI recommends B2B products. A complete, well-maintained G2 profile with recent reviews isn’t just for your sales team’s social proof — it’s training data for the AI that your buyers are consulting.
Other signals that feed AI recommendations:
LinkedIn content. LinkedIn recently reported being the #1 most-cited domain for professional queries across AI search platforms. Your expert content on LinkedIn is increasingly being cited by AI systems. This is a case where employee-generated content and thought leadership have a direct, measurable impact on AEO.
Mentions in reputable publications. Guest posts, expert quotes, original research cited by others — these are authority signals for AI, just as they were for SEO. But for AEO, they’re often more important because AI systems prioritize consensus across multiple credible sources.
Community presence and user-generated content. Reddit threads, community forums, and Slack discussions are all sources that AI scrapes for recommendations. This is where your power users become your AEO army.
Integration ecosystem mentions. If you’re mentioned on partner websites, integration directories, and marketplace listings, you’re building citation density that AI picks up. (This connects directly to the GTM ecosystem model — ecosystem actors generate citation signals you can’t create alone.)
José Velez shared an example of Indie Campers ($140M+ ARR): “We grew organic revenue 8.8% YoY and AI search revenue 254% in the first 5 months of working together. But to rank for competitive prompts specifically like “best RV rental company” and “RV rental Tomorrowland”, the strategy was completely different from the under-explored formats. Competitors were already covering those, so we had to create specific types of content tailored to the patterns the models were citing and secure earned mentions on the third-party sites the models pull from.”
Step 6: Monitor and Iterate (This Is Not “Set It and Forget It”)
If there’s one mistake that will waste all the work above, it’s treating AEO as a one-time project. AI outputs are probabilistic, not deterministic. They shift with model updates, competitor content changes, and citation pattern changes.
What to track:
Share of Model: How often your brand is mentioned and recommended across AI platforms. Track monthly against competitors.
AI-referred traffic and conversions: Set up referral tracking for chat.openai.com, perplexity.ai, and other AI sources in your analytics. Measure not just visits but conversion rate and pipeline contribution.
Framing accuracy: Is AI describing your product accurately? If it’s wrong or outdated, you need to fix the source material. Several companies are now actively correcting misinformation that AI surfaces about their products.
Citation sources: Which third-party sources does AI cite when mentioning you? Strengthen the ones that drive good positioning; fill gaps where competitors are getting cited and you’re not.
The companies that are winning at AEO treat it like a living system - more like product marketing than like a content calendar. They test prompts weekly, refresh key pages quarterly, and track their Share of Model the same way they’d track pipeline metrics.
The AEO Stack: Tools to Get Started
The tooling landscape is evolving fast, but here’s a practical starting point:
AI visibility monitoring:
Profound — tracks brand mentions and citations across ChatGPT, Perplexity, Gemini; backed by $35M Series B from Sequoia
Ahrefs Brand Radar — tracks brand visibility in AI search across major chatbots
HubSpot Search Grader — free tool for a baseline AI visibility audit
Content optimization for AI:
AirOps — content engineering platform helping teams see highest-impact pages and act on AI visibility data
Surfer SEO — increasingly focused on AEO alongside traditional SEO
Manual testing (free, effective, start today):
Run prompts yourself across ChatGPT, Perplexity, Gemini, Google AI Mode
Track AI referral traffic in your analytics (look for chat.openai.com, perplexity.ai as referrers)
Set up a monthly “AEO audit” cadence — 30 minutes, 15-25 prompts, document what you find
Here are also Google’s recommendations for optimizing your content for performance in AI Overviews.
AEO Is a GTM Capability, Not a Content Project
If there’s one thing I want you to take away from this article, it’s this: AEO is not a one-person content project. It’s a cross-functional GTM capability.
It touches product marketing (how your product is described and positioned), content (what you publish and how it’s structured), brand (what third parties say about you), product (what users share and how integrations are listed), and sales (the language used in decks and on calls ends up in the digital ecosystem AI scrapes).
LinkedIn recognized this when they formed a cross-functional “AI Search Taskforce” spanning SEO, PR, editorial, product marketing, and brand. They saw their B2B non-brand traffic drop up to 60% on awareness-driven topics and realized traditional SEO alone couldn’t solve it.
An important part of your AEO strategy is localization. Your buyers use AI chatbots in their natural language, so make sure that your AEO content is available and translated in the languages of your target markets.
“AEO is a compounding channel. Early wins come from listicles and competitor alternatives content, but the real compound effect shows up around months 4-6 once the models start consistently associating your brand with the category.” - José Velez, Founder & CEO, Reach
The window to build early AEO advantage is open right now. With 89% of companies not yet AI-ready, the early movers will own disproportionate share of AI recommendations in their categories. Webflow’s journey from 1.2% to 60% category share didn’t happen overnight — but it happened in months, not years.
As Dima Durah said, we’ll soon be optimizing not just for answer engines but for autonomous AI agents that browse, compare, and purchase on behalf of buyers. Companies with machine-readable, structured product truth will have a massive head start.
Here’s your minimum viable next step: Run 10 prompts today.
Open ChatGPT. Type the questions your buyers ask. See what comes back. That 30-minute exercise will tell you more about your AI visibility than any strategy deck.
Then come back here and work through the 6-step playbook. You’ve got everything you need to start.
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