What Is GEO and AEO? How AI Is Changing B2B SEO in 2025

I hate to admit it, but as someone who has been doing SEO since 1998, I’m old enough to remember when ShoeMoney pronounced that SEO Was Dead in 2005. Of course, SEO has been the zombie that won’t die ever since. 

This humorous history of “SEO Is Dead” comments reminds us that “past is prologue” and hyperbole is no stranger to the SEO industry. So when I start hearing people in 2025 proclaim SEO Is Dead (again) because of all the new AI-powered changes, it makes me laugh a little.

I’m certainly not the only one who doesn’t believe the death of SEO is near. For further context, you may want to check out Lilly Ray’s recent article on Search Engine Land.

That said, I have never seen the amount of change and disruption to this industry at any time in the last 27 years. And yes, most of the turmoil has been driven by AI technology advances and the resulting changes to user behavior.

GEO and AEO: Still Just SEO?

This year has even given birth to the rise of two new acronyms (for better or for worse): Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). I understand the intention behind these terms, and we use them today to categorize the changes we are seeing, but to me, this is all still SEO.

Fundamentally, not much has not changed. Your potential customers are searching for things. When they’re searching for your products or services, you want them to find you. My job is to make sure they do. That’s SEO.

I’m also old enough to know when swimming upstream is pointless. These new terms will be adopted to draw distinctions between strategies, tactics and outcomes (as well as for marketing purposes). From that perspective, it's useful to define them, especially as they relate to how SEO and findability has and is changing this year. 

For the record, I like Mike King’s umbrella definition of all these terms: Relevance Engineering. But since we already have enough to discuss, I’ll leave that out of our vernacular for today.

There are a lot of articles right now that explore these changes to SEO. I’m going to try as much as possible (although it's not totally possible) to stay high level on technical issues and stay as focused as possible on strategic direction for B2B SEO marketers. 

First, let’s dive into these new acronyms, what they mean, and what it means for SEO.

What Is Answer Engine Optimization (AEO)?

Answer Engine Optimization (AEO) is the practice of optimizing content to appear directly in AI-generated responses (most often in search engines) and featured snippets. It focuses on providing immediate, authoritative answers to specific user queries. 

Unlike traditional SEO that drives traffic to websites, AEO also optimizes for zero-click visibility, where users get complete answers without leaving the search platform. In some cases these ranking opportunities feature links that can drive traffic, but in many instances they do not. The zero-click nature of these results adds an additional layer of complexity to measuring success.

AEO Success Metrics

Traditional metrics like organic traffic need to be expanded in AEO strategies. With AEO, success measurements focus on visibility within AI-generated responses and brand authority establishment through citations. 

It’s important to understand that these metrics measure potential brand awareness opportunities that can lead to direct traffic, search traffic through a subsequent brand related query, or influence a purchasing decision based on perceived authority.

Additional AEO success metrics include:

• Featured snippet appearances across target keyword sets
• Voice search result inclusions for conversational queries
• AI Overview citations when Google references your content
• Brand mention frequency in zero-click search results
• Answer accuracy rates when AI systems quote your content

By taking a wider view of what constitutes success, we can recognize activities that contribute value to the sales funnel, even though leads and revenue may not be directly attributable to them. This is especially true for B2B customer journeys, where attribution is already challenging because there are multiple influencers within a single organization.

What Is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) can be defined as the strategic process of formatting, structuring and marketing content so AI platforms like ChatGPT, Perplexity, Claude, Grok, and Gemini can easily understand, extract and cite it when generating responses. GEO represents a fundamental shift from optimizing for page-level search engine rankings to engineering relevance for passages within AI reasoning systems.

The Technical Foundation of GEO

GEO operates on different principles than traditional SEO systems (although there is an overlap with modern search engine architecture in terms of relevance). Instead of targeting keyword rankings with webpages, GEO is based on semantic relevance of passages within the vector-based retrieval systems that AI platforms use to identify and synthesize information.

Successful GEO content engineering focuses on creating information that supports AI reasoning processes, rather than just human reading patterns. In other words, there needs to be a balance between writing for human beings and writing for visibility within these systems. Content must survive passage-level competition in probabilistic retrieval systems to be effective.

Core GEO technical concepts (some are also applicable to AEO) include:

• Vector embeddings: AI systems convert content into numerical representations for semantic similarity matching. The relative distance between topics, words and entities in this vector space is how AI systems understand how terms are related to each other.
• Passage-level optimization: Content structured in sentences, paragraphs or passages that can be independently evaluated for relevancy.
• Query fan-out compatibility: Content addressing multiple related queries that AI systems generate from single user inputs. These AI systems search for information across multiple related queries in order to return a comprehensive answer.
• Entity recognition: Clear subject-predicate-object relationships that AI can parse and understand. If you don’t know what entities are or how to recognize them, I recommend the following article.
• Reasoning chain support: Logical information flow that enables AI to build multi-step responses.
• Semantic completeness: Each passage provides value without requiring additional context.
• Explicit concept relationships: Connections between ideas stated directly rather than implied.
• Conversational optimization: Natural language patterns matching AI interaction styles.

GEO Success Metrics

Much like AEO, many of the citations or brand mentions that occur in the output of these GEO systems have much lower CTRs than traditional search results. That’s because they either don’t have links or have links that aren’t prominently displayed. 

The upside is that initial research suggests that the traffic that does come from these citations is more likely to be engaged and result in conversions, so it might be more valuable. Additionally, traffic from LLMs, especially ChatGPT, continues to rise monthly for most of my clients. 

However, just like AEO, we need to look beyond direct traffic metrics to get a complete picture of the impact GEO systems are having on our customers and potential customers. The issue is that citations in these systems are harder to measure. 

Currently there is no data available as to what user input prompted a referral. Essentially, there is no keyword data (although keywords are an oversimplification of how most users are interacting with these systems). 

Also, the same prompt tends to elicit different results for different users and they are highly volatile in terms of which sites are listed. This recent study by Profound illustrates how often these results change.

This underscores the main challenge for GEO right now: It’s very much a moving target with limited signals that show success, not much historical data to compare those signals to, and a high rate of change for the results we’re trying to measure.

SEO vs. AEO vs. GEO: Strategic Framework

Understanding the distinctions between traditional SEO, AEO and GEO enables B2B marketers to develop targeted strategies that address both current search behavior and emerging AI-driven discovery patterns.

Primary Focus Differences

Traditional SEO optimizes webpages (with text, video and image content) to rank higher in search engine results pages, driving traffic through improved visibility for targeted keywords. Success measures include organic traffic growth, keyword rankings and conversion rates from search visitors.

Answer Engine Optimization (AEO) structures content for direct inclusion in AI-generated responses, featured snippets and voice search results. Success measures focus on zero-click visibility, citation rates, brand authority establishment through AI references as well as traffic from links where they occur.

Generative Engine Optimization (GEO) engineers content for citation across multiple AI platforms simultaneously, focusing on semantic relevance within vector-based retrieval systems. Success measures include cross-platform citation rates, brand mentions in AI responses, source authority recognition (links to your website) and traffic from those links.

Target Platform Comparison

While here I’m trying to make a clear distinction between AEO and GEO, in practice they are often used interchangeably. I’m splitting hairs for the benefit of folks who want to make a distinction between the two, but ultimately, that distinction has limited value (except for discussions like this one).

In fact, Google has announced that AI overviews and AI mode are built from custom Gemini models that work with traditional search systems. There is a lot of shared technology or related concepts across all of these systems.

That’s why most of the optimization principles for AEO and GEO are based on the same concepts. These are relatively new acronyms and despite the distinction I am making between the two for academic purposes, it really isn't necessary. In fact, it’s still all just SEO to a lot of people like me. The differences between these acronyms are less important than what we do to optimize for them and how that might differ from traditional search.

Implementation Strategy Integration

Successful B2B organizations implement integrated strategies that maximize visibility across traditional search engines, AI-powered answer systems, and generative AI platforms. Let’s explore what that means.

How AI Is Revolutionizing B2B SEO

New Technology Requires New Optimization Tactics 

AI search platforms operate on principles that fundamentally differ from traditional search engine algorithms. AI systems dynamically retrieve relevant content passages (sentences or paragraphs) from the web at query time, ensuring responses are grounded in real-time information rather than static model knowledge. This means that for AEO and GEO systems, passage optimization is more important than page optimization.

This has numerous implications for B2B content strategy. Google's AI Overviews, AI Mode and LLMs like ChatGPT use passage-level ranking models that evaluate specific document sections rather than entire pages. This means weak or unfocused content sections get ignored even if the overall page ranks well in traditional search results.

Key technical concepts to understand (or at least be aware of):

• Vector embedding-based retrieval: AI systems match queries and documents based on semantic similarity rather than keyword presence.
• Query fan-out expansion: Single user queries generate dozens of related subqueries to retrieve comprehensive, intent-aligned content.
• Probabilistic ranking: Content selection based on semantic relevance and reasoning support rather than deterministic ranking factors.
• Real-time grounding: Dynamic validation of information across multiple sources prevents hallucinated responses.

There is a lot of information out there about how these systems work, including this excellent article from Mike King, so I won’t dive too deep here. Ultimately, just understand that we need different tactics to optimize for these AI systems.

User Behavior Transformation Patterns

B2B buyers are fundamentally changing how they research solutions. Clickthrough rates in organic search results have gone down significantly as Google AI Overviews have been rolled out across more keywords over the last 14 months. 

Conversely, traffic to LLM platforms like ChatGPT has increased significantly. This behavioral shift requires B2B marketers to understand new discovery patterns and content consumption preferences.

Emerging B2B research behaviors include:

• Conversational query patterns: Perplexity CEO Aravind Srinivas says searches are averaging 10–11 words on Perplexity versus 2–3 words on traditional Google search.
• Zero-click preference: 58.5% of Google searches now end without clicks as users get complete answers from AI responses (and that was in 2024). Research from Bain and Company finds that 80% of consumers rely on these “zero-click” results at least 40% of the time.
• Multi-modal discovery: Integration of video, audio, charts and text content for richer research experiences.
• Personalized synthesis: AI systems adapting responses based on user history, preferences and behavioral signals. This means users will get customized answers, making it harder to measure optimization success.

The Authority Signal Evolution

Traditional authority signals like backlinks and domain authority are becoming less predictive of AI search visibility. AI citations cannot be explained by traditional website metrics because AI systems evaluate content quality through different mechanisms. 

Admittedly, this is a moving target and measuring success is not completely straightforward. Various tools are being developed to measure optimization success, but they all have limitations.

 Based on my observations to date, authority indicators for AI systems include:

• Semantic consistency: Content demonstrating clear understanding of topic relationships and industry terminology.
• Citation-worthy formatting: Information structured for easy extraction and reference by AI systems.
• Expert attribution: Clear author credentials and institutional affiliations supporting content credibility. This also maps back to entity recognition and E-E-A-T signals.
• Factual accuracy: Regular content updates and source verification supporting AI system confidence.
• Entity recognition: Clear connections to established knowledge graph entities AI systems can validate.
• Brand mentions: Frequency of brand mentions across authoritative sites relevant to the topic.

Impact on B2B Sales Cycles

AI search optimization is fundamentally altering B2B sales cycles by changing how prospects discover and evaluate solutions. Traditional lead generation through content downloads and form fills is being supplemented by AI-mediated discovery where prospects learn about companies through AI citations and references.

Early adopters report that AI search traffic demonstrates higher intent and conversion rates. A recent study by Amsive showed that LLM traffic converts at 3.76% versus 1.19% for organic search traffic—a 216% improvement in conversion performance.

So what are the implications for the B2B sales cycle?

• Earlier brand awareness: Prospects encounter companies through AI citations before visiting websites. This can make attribution harder if not impossible to fully understand.
• Compressed research phases: AI synthesis reduces time spent comparing multiple sources.
• Authority-based selection: Companies referenced by AI systems gain a perceived credibility advantage.
• Intent signal changes: Traditional tracking methods miss AI-driven research activity.

Go Forward B2B SEO Strategies and Tactics

Continue Current SEO Best Practices

Despite all the changes, SEO is not dead. SEO continues to be the number one acquisition channel for most of our clients. This means you should still pursue the following SEO tactics:

• Technical SEO best practices: Constantly evaluate the technical performance of your site with tools like Google Search Console and Bing Webmaster Tools. Other paid tools like Screaming Flog, Botify and SEOClarity can also help identify issues that are adversely affecting your performance.
• Page-level content optimization: Create keyword-focused page titles and metadata that communicate value to users. Merge current tactics for optimizing page-level content with passage-level optimization best practices.
• Internal link optimization: Ensure that internal link connectivity supports findability and underscores the hierarchical nature of your content.
• Conversion optimization: Test and continue to test the best way to encourage user engagement and optimize customer journey velocity while reducing friction as much as possible.
• Digital PR: Leverage digital PR  through an E-E-A-T lens that creates link recognition, citations and brand awareness.

Despite lower clickthrough rates and decreases in overall SEO traffic, traditional SEO still outperforms LLMs for most companies. Traditional SEO still refers a significant amount of traffic, so you need to strike a balance between your SEO efforts and your AEO and GEO tactics. Implement slowly and test changes to your site in order to mitigate risk to existing performance.

As an additional note that underscores the importance of traditional SEO rankings, Ahrefs is reporting that 76% of AI overview citations pull from Top 10 pages.

Passage-Level Content Engineering

Modern AI search success requires optimizing content at the passage level rather than page level. The relevance of a single sentence to a topic is now an important consideration for LLM citations. This represents a fundamental shift for some writers from the traditional page-focused approach to content engineering that supports AI reasoning processes.

Each content passage must function as a semantically complete unit capable of answering specific user questions without requiring additional context. This means restructuring existing content to create standalone information blocks that AI systems can independently evaluate and combine. The challenge is to do that without sacrificing your content’s ability to connect with users and effectively communicate brand value.

To optimize at the passage level, content creators need to ensure:

• Semantic completeness: Each segment provides value without requiring additional context.
• Clear topic sentences: Opening statements that immediately identify the passage's core message.
• Entity-rich language: Consistent terminology aligned with Google's and Wikipedia's knowledge graphs.
• Structured data: Implemented where applicable.
• Header structure alignment: H2 and H3 tags that reinforce passage-level semantic organization.

AI platforms determine content relevance through cosine similarity calculations between vector embeddings, rather than keyword matching. This mathematical approach to content evaluation requires understanding how AI systems convert text into numerical representations for comparison.

Vector optimization strategies include: 

• Semantic keyword networks: Related terms and concepts rather than isolated keyword targeting.
• Topic cluster architecture: Comprehensive coverage demonstrating topical authority across related concepts.
• Entity relationship mapping: Clear connections between industry entities, products and services.
• Natural language patterns: Conversational phrasing matching user query formulations

Tools have started to emerge that measure cosine similarity between content passages and keywords or topics. I won’t specifically endorse any of these at this time, but you should be experimenting with these tools and testing whether optimizing for cosine similarity improves your performance. 

Using topic cluster architecture, semantically related terms, entity inclusion and natural language patterns are all tactics that have been important for SEO for a number of years. Those tactics are now working at the passage level instead of at the page level.

Query Fan-Out Compatibility

Google's AI Mode and ChatGPT both use query fan-out processes that generate dozens of related subqueries from single user inputs. Content must address not only the primary user question but also the comparative, exploratory and implicit queries AI systems generate during retrieval. These queries may change on a per-user basis based on personalization factors.

This requires content architects to think beyond individual keyword targets toward comprehensive query landscapes surrounding user intent. Successful content addresses multiple facets of user questions through structured sections covering definitions, processes, comparisons, benefits and implementation considerations.

Fan-out optimization tactics include:

• Comparative analysis sections: X versus Y evaluations addressing decision-making queries.
• Feature breakdown content: Detailed explanations supporting "how does X work" type queries.
• Alternative solution coverage: Competitive landscape acknowledgment and positioning.
• FAQ integration: Natural language questions and complete answers addressing related concerns.

If you want to start experimenting with fan-out queries, Screaming Frog is one of many  interesting options. This article by Metehan Yesilyurt explains how you can use Screaming Frog to run query fan-out analysis.

Many of the tools on the market today are brand new. Stay tuned for the industry to release a ton of new tools specific to cosine similarity measurement and fan-out query identification.

Additional Content Optimization Recommendations

• Unique research: First-party or unique research and statistics whenever possible.
• Statistics: Proper attribution and recency indicators.
• Comparative listicle formats: Structured pros/cons analysis. 
• Opening paragraph optimization: Direct answers to core questions within initial content.
• Source attribution requirements: References to established authorities and statistical sources.
• Step-by-step workflows: Easy instructions and methodology documentation.
• Expert quotes: Recognized authorities within relevant industries.
• Factual accuracy: Verification through multiple authoritative sources.
• Regular content updates: Maintaining information currency and relevance.
• Conversational language patterns: Natural phrasing matching user query formulations.
• Review platform integration: Customer testimonials and third-party validation.
• User perspective emphasis: Real-world implementation examples and case studies.
• Topical authority: Comprehensive coverage rather than keyword density.
• FAQ development: Natural language questions and complete answers addressing user intent variations.

Schema Markup for AI Understanding

Structured data serves as the critical bridge between human-readable content and AI interpretation, with specific schema types proving most effective for AI search optimization. Implementation goes beyond basic markup to creating comprehensive entity relationships that AI systems can confidently parse and utilize.

Priority schema implementations for B2B companies include:

• Article schema with proper author attribution
• Organization schema for entity recognition
• FAQ schema for question-answer content
• How-to schema for process-oriented content
• Product/service schema for commercial offerings

In general, it makes sense to use whatever appropriate schema is available to describe your content with an emphasis on schema types that are directly supported by Google and Bing.

In fact, Bing has confirmed that their LLM models use structured data and so has Google.

Advanced schema implementation includes:

• Entity linking: Connections between company entities, industry terms and service categories.
• Author markup: Professional credentials and expertise indicators supporting E-E-A-T evaluation.
• Review schema: Customer testimonials and case study integration for authority signals.
• Event schema: Webinars, conferences and industry participation documentation.
• Local business schema: Geographic service area and location-specific optimization.

Third-Party Sites and Social Media Marketing

Another aspect of digital marketing to factor into your go-forward SEO/AEO/GEO strategy is how you’re being talked about on third-party sites. Some recent studies have shown that certain websites are most likely to appear in LLM citations and AI search results. These sites include:

• Reddit
• Quora
• Wikipedia
• LinkedIn
• YouTube
• Forbes
• Gartner
• G2

Additionally, many of these sites have strong visibility in traditional organic search results, especially Reddit. Ensuring that your brand appears on these sites whenever possible lends itself to additional citations, even if it’s just a brand mention without a link. 

You should really already be engaged in conversations on Reddit and Quora, where your potential and existing customers are having conversations about your brand. Any SEO/GEO/AEO benefit from those sites should be a bonus as opposed to the main focus. 

Remember that these sites are not places to market your products and services. They are places to contribute value to the community. By doing this, you will earn the kinds of mentions and links that lead to citations.

Each of the sites that I listed (and there are even more) needs a dedicated strategy with an understanding of what components of that strategy influence SEO and LLM performance. Getting an SEO professional involved in those strategies will help ensure that you’re maximizing your return on investment.

Technical Considerations

Optimizing your ability to be indexed and cited by AI systems involves various technical considerations. One of today’s big issues is that LLMs have a hard time understanding Javascript. While this is likely to change soon, currently they may not be able to index your content if you’re using Javascript.

Here are some other technical considerations worth considering: 

• Server-side rendering: May be a solution for Javascript indexing issues.
• LLM.txt files: Despite Google announcing that they don’t support the llms.txt protocol, others have reported they are seeing LLMs crawl these files. Whether they result in better indexing has yet to be proven, but it's possible this will prove to be valuable.
• Robots.txt: Make sure you aren’t inadvertently blocking LLMs from indexing your content.

Recap: What You Can Do Right Now

Here’s high level recap of everything we have discussed that I would recommend incorporating into your SEO/GEO/AEO strategy:

• Keep doing the SEO best practices that have been working for you.
• Augment your content optimization process with tactics focused on identity inclusion, topic-based site structure, passage-level cosine similarity analysis and fan-out query inclusion.
• Focus on creating unique content with incremental value over existing resources that rank well or perform well in LLMs for topics that are important to your business (be better than your competitors and have something unique to say).
• Utilize schema markup wherever it makes sense while prioritizing schema types that Google and Bing support.
• Focus on creating E-E-A-T signals through digital PR and authorship of expert materials that underscores why your brand is a trusted resource within your industry. 
• Engage and market to third-party authority sites that are frequently cited in AI systems as part of your overall marketing efforts beyond SEO (with an understanding of how those channels affect your performance in search engines and LLMs).
• Stay tuned for what’s next. The changes are going to continue and you need to spend a time every week paying attention to new developments in the industry that might change how your customers find you.

Conclusion

If all of this seems like a lot to consider, that’s because it is. The search landscape is changing very quickly and it continues to evolve at an unprecedented pace. For example, ChatGPT5 just launched (to underwhelming reviews), Claude recently launched Opus 4., and Google just launched the Nano Banana image editor. Those launches are just the tip of the iceberg across the AI landscape. Additionally, AI browsers are starting to come online with Perplexity launching Comet and Open AI about to launch their own browser. These browsers will fundamentally change how people interact with the web. 

As if that wasn’t enough, ChatGPT Agent just launched. Additionally, the rise of MCP servers is changing the way that AI agents are working and what they can do, and that’s changing how people are working.

To say things are crazy right now is understating the obvious.

So that begs the question, how do we respond to the changes in a way that maximizes our chances of success? Embrace the change, stay curious, do your homework, and ultimately, I believe, there will continue to be opportunity for success.