Is SEO the same as AEO? We think not | Discovered Labs

Discover the fundamental differences between traditional SEO and Answer Engine Optimization (AEO). Learn why your SEO strategy might not be enough for AI search visibility and how to adapt for the new era of AI-powered discovery.

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The shift in B2B discovery

AI search has fundamentally changed how B2B buyers discover products and services. Traditional SEO strategies that have worked for the past decade are no longer sufficient if you want to be recommended in AI-powered answers.

Key statistics reshaping search

1 in 5 Google searches included an AI summary in March 2025, enabling what we call "zero-click research"
48% of B2B buyers use AI search while evaluating vendors
50% projected drop in traditional search by 2028 as users shift to AI assistants
3-4x higher conversion rates for visitors coming via AI search compared to traditional organic traffic
In a recent study, Ahrefs found that while AI-referred traffic accounted for only 0.5% of their website traffic, it generated 12% of their total signups - a 23x higher conversion rate than traditional traffic.

Key Insight
If you haven't adapted your search strategy for AI, your company could be invisible to a rapidly growing segment of the market - even if you've been doing traditional SEO for years.

Traditional search vs AI search: a fundamental shift

Traditional search experience

In traditional search, the user journey follows a predictable pattern:

Users search with short queries (3-7 keywords)
They're presented with a list of blue links (the SERP)
Users click through various links to conduct research

This is the model we've optimized for over the past 20 years through SEO.

AI search experience

AI search represents a completely different paradigm:

Users have conversations with AI assistants
They provide extensive context and constraints
The AI conducts web searches behind the scenes
It synthesizes information from multiple sources
Returns a personalized answer based on the user's unique situation

This shift from short queries to conversations and from random blue links to personalized answers changes everything about how we need to approach search optimization.

Why SEO ≠ AEO: the core differences

1. From chasing clicks to earning citations

Traditional SEO	AI Search (AEO)
Chasing clicks from ranked links	Earning mentions and citations in personalized answers
Optimizing entire pages	Creating extractable 40-80 word passages
2000-3000 word articles	Information-dense answer blocks
Metrics: Impressions, clicks, positions	Metrics: Mention rate, citation rate, share of voice

2. Query fan-out: how AI models search

Unlike traditional search engines that take your query verbatim, AI models transform user prompts into dozens of sub-queries through a process called "query fan-out." These models:

Create multiple search variations with filters and operators
Search for specific facts across different phrasings
Pull relevant snippets from multiple sources
Merge information into one cohesive answer

This is fundamentally different from traditional SEO where you optimize for specific keywords. With AI search, you need to ensure your information is discoverable across many different phrasings and angles.

3. From fixed positions to personalized rankings

In traditional SEO, we chase stable page positions - trying to rank number one for specific keywords. But in AI search:

There's no single "position one" to hold
Rankings change based on user context and constraints
Two users with similar queries but different budgets or company sizes get completely different recommendations
Memory and conversation history influence results

This means smaller companies with lower domain authority can become the recommended solution if they optimize correctly for AI search - something impossible in traditional SEO where incumbents dominate.

4. Beyond backlinks: cross-source agreement

Traditional SEO relies heavily on backlinks as the primary authority signal. AI search looks for something different:

Corroboration across multiple sources: Review platforms, directories, press, documentation, communities, Wikipedia
Consistency of facts: If multiple third parties say one thing about your pricing but your website says another, AI trusts the consensus
Natural language mentions: Authentic discussions in forums and communities carry significant weight

Think of it this way
Your website is like your resume where you say good things about yourself. In AI search, the models are calling your references to verify your claims. What others say about you matters more than what you say about yourself.

The three surface areas of modern search strategy

A winning search strategy in 2025 and beyond must consider three primary surface areas:

1. Traditional discovery (SEO)

Being discoverable via web search by humans searching keywords in traditional search engines. This remains important but is no longer sufficient on its own.

2. AI citations (AEO)

Having rich, verifiable, and consistent facts that can be cited by AI models. This requires:

Structured, extractable information
Corroboration from third-party sources
Consistency across all web properties

3. Training data integration

Getting your most important facts baked into the actual AI models themselves so future versions know about your company without needing web search. This provides long-term defensibility.

The four-step AEO playbook

Step 1: Answer modelling

Move beyond keyword research to understanding your "prompt universe" - all the ways buyers could discover you through AI models:

List questions and prompts where your product should be recommended
Consider conversational queries, not just keywords
Pull language from sales calls, support conversations, and reviews
Test across different personas and intent levels
Monitor performance across multiple AI platforms (ChatGPT, Claude, Perplexity, etc.)

Step 2: Technical excellence

Because we're optimizing for machines, technical foundations are non-negotiable:

Develop a single source of truth: Maintain consistent facts about pricing, features, and differentiators
Implement comprehensive schema markup: Help AI understand your content structure
Ensure perfect crawlability: Fix bad URLs, 404s, and canonical issues
Maintain consistency: Align information across your site and third-party platforms

Step 3: Content operations

Create content designed for AI consumption:

Short, quotable units: 40-80 word answer blocks, not 2000-word essays
Source-backed claims: Include verifiable facts and citations
Retrieval-friendly structure: Use clear headings, lists, and tables
High velocity: Ship multiple pieces of content daily, not weekly or monthly
Fresh information: Regular updates signal relevance to AI models

Step 4: Trust engineering

Build authority through third-party validation:

Seed authentic discussions: Engage genuinely in forums and communities
Manage third-party profiles: Keep review sites, directories, and marketplaces updated
Create original research: Provide novel data that AI models want to cite
Monitor competitor mentions: Identify where competitors appear but you don't

Measuring success in AI search

Traditional SEO metrics don't tell the full story. For AI search optimization, track:

Leading indicators

Mention Rate: How often you appear in AI answers
Citation Rate: How often you're cited as a source
Share of Voice: Your visibility relative to competitors
Sentiment: How you're positioned and described

Business metrics

AI-referred traffic: Quality over quantity
Conversion rates: Typically 3-4x higher than traditional organic
Pipeline influence: Track AI's impact on decision-making

The opportunity ahead

We're still in the first innings of AI search. While trillion-dollar companies battle for dominance, the playbook is still being written. Most companies haven't adjusted their strategies yet, creating a massive opportunity for early movers.

The companies that adapt now - that understand the fundamental differences between SEO and AEO - will secure lasting advantages in how AI systems understand and recommend their products.