Generative Engine Optimization (GEO): What It Means for Sales and Marketing Teams

Generative Engine Optimization (GEO) is no longer just a buzzword. If you’re working in sales and marketing, it’s imperative that you no longer ignore this term. It’s not just a trend, it’s now a strategy that businesses need to adapt to scale in 2025 and beyond.

To illustrate, here’s a question for you: When you conduct an online search, do you still use search engines like Google, Yahoo, or Bing? Or have you already started using AI tools like ChatGPT to recommend resources?

For business users who aim to establish online visibility, this customer behavior shift is more than a useful pursuit. Because this new behavior—relying on AI-generated answers instead of scrolling through links—is exactly what gave rise to GEO.

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) is the practice of optimizing your content to appear in responses generated by AI-powered search engines and chatbots like ChatGPT, Claude, Google’s SGE (Search Generative Experience), and Bing Chat. 

Compared to traditional search engine functions, generative engines summarize and synthesize information from multiple sources to provide direct, conversational answers for users’ queries 

In short, ranking on the very first page of Google is no longer the sole measure of success. Your content needs to be selected, cited, and synthesized by AI models too. Why? Because your customers are using AI, most probably. 

To reach them, you have to make AI recognize you.

The evolution of GEO: A deeper dive into its capabilities

Between the years of 2022 and 2024, AI tools emerged quickly as an experimental technology. Early adopters tested ChatGPT for quick answers. This process eventually evolved to be used in daily workflows across different industry processes. 

During this shift, users began asking complete questions. Instead of using fragmented and quick phrases such as “CRM software pricing comparison,” many users began asking complete questions for clearer contexts such as: “What is the best CRM for a 50-person B2B sales team with limited resources/budget?”

Traditional search engines will respond to this query with a list of links and articles. Users are left to make their own decisions, to sift through multiple tabs, do the manual work of comparing articles and cross-reference reviews in order to come up with a synthesized conclusion. This takes hours of process, and requires significant analytical and evaluation skills, separating useful information from content marketing designed entirely for SEO. 

Generative engines eliminate this challenge entirely. They can supply a single and information heavy response that can directly address the question, while incorporating relevant factors users specified. If prompted correctly, generative engines can even provide comparison tables within seconds.

This represents a shift or transformation in how people expect to receive information: complete and decisive. But would this mean that SEO strategies became obsolete entirely?

The AI content overview 

Let’s dive deeper into the heart of it all—the “AI Content Overview” of Google search engine:

• In April 2025, Ahrefs analyzed over 300,000 keywords and found that AI Overview in the search results correlated with a 34.5% lower average clickthrough rate (CTR) for the top-ranking page, compared to similar informational keywords without an AI Overview.
• Organic click-through rates (CTR) for informational queries featuring Google AI Overviews fell 61% since mid-2024, while paid CTRs on those same queries plunged 68%, according to the latest study by Seer Interactive.

For certain query types, mostly informational and comparison searches, the decline was even steeper. Users simply weren’t visiting websites anymore. AI summaries proved enough and gave them straight, direct answers.

This created an existential question for marketing teams: if potential customers never reach your website, does your content strategy even matter?

The answer is yes. But only if that content is optimized for how AI systems discover, interpret, and present information. 

Here’s what happened next:

• This realization sparked the development of GEO as a distinct discipline. 
• Early practitioners began reverse-engineering which content characteristics led to inclusion in AI responses. 
• They discovered that many traditional SEO tactics like keyword stuffing, backlink schemes, and technical tricks were irrelevant or even counterproductive for AI visibility.
• What mattered instead was content clarity, authoritative structure, direct answers to specific questions, and semantic relationships between concepts. 
• The content that performed best with generative engines was often the same content that genuinely helped human readers. In a sense, this symbolized a return to fundamentals after years of gaming search algorithms.

GEO isn’t a rejection of SEO. It’s the next evolutionary stage. 

Just as SEO evolved from simple keyword matching to complex algorithms considering hundreds of ranking factors, GEO represents optimization for a world where the search result itself is a conversation, not a list of links.

How generative engines work

How GEO operates differs entirely from how traditional search engines usually conduct content strategies. Generative engines are built on large language models (LLMs) which are neural networks trained on vast datasets comprising billions of web pages, books, articles, and documents. 

When you ask a question, you are not simply triggering a simple database lookup. Here is what happens.

They analyze intent, not just match keywords

Once a query is received, the model will interpret this query by analyzing intent instead of just showing websites that match keywords related to your search.

So when you ask “What’s the ROI timeline for marketing automation implementation,” the system understands you are asking about potential financial return, timeframes, and a specific technology category. 

Relevant information is identified

From its training data or, in more advanced implementations, from real-time web searches. The model will identify the most relevant information related to your search. It will use semantic understanding to find content that addresses your actual question, even if the content uses different terms. 

For example, an article about “time-to-value for automated marketing platforms” could be selected even though it doesn’t contain your exact words.

Information from multiple sources are synthesized

The AI will extract information from multiple sources to create the most coherent response. It will try to extract relevant facts, analyze consistency across sources, and generate new answers in natural language that directly answer what you are asking. 

This synthesis process is where many businesses lose visibility:

• The AI doesn’t display your webpage or link to your article prominently. 
• Instead, it extracts the information it needs, and often without direct attribution. Then, it incorporates it into a response that may also include insights from a dozen other sources. Your brand essentially becomes one ingredient in a larger answer, but not the answer itself.

So even if your content is factually correct, comprehensive, and research-heavy, if it’s not structured in a way that generative engines can easily parse, extract, and understand, it becomes invisible. 

The AI will prioritize sources that present information more clearly, even if those sources are less comprehensive or authoritative than yours.

Why GEO matters for sales and marketing teams

Generative AI is transforming how potential customers discover products, research solutions, and make purchasing decisions. For sales and marketing teams, this shift has profound implications.

GEO addresses change in buyer behavior

Instead of clicking through multiple search results, users now receive comprehensive answers in a single AI-generated response. 

So if your brand isn’t included in these responses, you’re basically invisible to a growing segment of your target audience.AI-generated answers minimize click-through rates to traditional websites, which warrant fewer opportunities for direct engagement. 

GEO compresses the customer journey

Generative engines can answer complex, multi-part questions that previously required visiting multiple websites. Marketing teams must adapt by creating content that addresses comprehensive user needs rather than focusing solely on specific keywords. 

Sales teams benefit when prospects arrive better informed and further along in their decision-making process.

GEO competitive advantage is still up for grabs

GEO is still emerging, and early adopters have a significant opportunity to establish dominance before best practices become industry standard. Companies that optimize for generative engines now will build momentum that becomes harder for competitors to overcome.

Related Reading: The Future of B2B Sales with AI: Moving Beyond Automation to Hyper-Personalization

GEO vs traditional SEO

Traditional SEO focuses on ranking in search engine results pages (SERPs) by optimizing for specific keywords, building backlinks, and improving technical site performance. 

Success is measured by rankings, organic traffic, click-through rates while ensuring that the content appears at the very top of relevant searches, to encourage users to click through your website. 

Meanwhile, Generative Engine Optimization, aims to have your content selected and cited by AI-generated responses. It’s not just about keywords. GEO requires optimizing for concepts, questions, and conversational queries. Success is measured by citation frequency in AI responses, attribution quality, and the accuracy with which AI systems represent your content.

Here is a more comprehensive breakdown.

5 best practices and strategies for generative engine optimization

Optimization for generative engines requires a strategic approach that understands both customer and AI behavior.

1. Focus on user intent

Generative engines excel at interpreting what users actually want to know, even when queries are vague or conversational. Your content must align with these underlying needs. As with all things sales and marketing, user intent is crucial. Here are some tips to help you ensure you’re on the right track with user intent:

• Research conversational queries. Tools like AnswerThePublic, AlsoAsked, and even Reddit reveal conversational patterns and specific phrasing your audience uses. Using these tools gives you the ability to pay attention to question modifiers like “how,” “why,” “what if,” and “best way to,” which are common in prompts to generative engines.

• Mirror generative engine’s preference. Make sure that you organize your content that directly answers specific questions. Be clear and precise. Start with the most direct answer to the primary query, then add layers in supporting details, plus context, and related information. 

• Optimize for question-based searches. Create dedicated sections that address common questions in your industry. Put some FAQ pages, create some how-to guides, and scenario-based contents.These contents usually perform well with generative engines. Format these sections so each question and answer pair is clearly delineated, making it easier for AI systems to extract and cite your expertise.

2. Use AI visibility tools

As GEO matures, there are some specialized tools that can help content creators optimize AI-generated responses. Using these platforms supply insights that some traditional SEO tools may not be able to accommodate. These tools can do the following:

• AI visibility tools typically crawl your content and simulate how generative engines might parse and prioritize it. 
• They identify gaps in topical coverage, suggest semantic improvements, and highlight opportunities to strengthen entity relationships within your content. 
• Some tools can even show you how often your content appears in AI-generated responses compared to competitors.

Example: Platforms like Clearscope, MarketMuse, and SurferSEO have evolved beyond traditional SEO optimization to include features specifically designed for generative engine visibility. 

3. Structure content for prompt-based queries

How you format and structure content dramatically impacts whether generative engines can effectively parse and utilize it. AI systems favor content that is clearly organized, logically structured, and easy to extract.

For example:

• Use descriptive headings (H1, H2, H3) that clearly indicate the topic of each section.
• Incorporate bullet points and numbered lists for steps, features, or key takeaways. 
• Keep paragraphs concise. Aim for three to five sentences maximum. This formatting makes it easier for AI systems to identify distinct concepts and extract relevant information without losing context.
• The most successful GEO content follows a pyramid structure: start with the most important information (the direct answer), then provide supporting details, evidence, and examples. 
• While “rankings” work differently in generative engines, there is a hierarchy to how content gets selected. You need to ensure that each major section for your content addresses a specific but well-defined user intent or question.
• Establish standalone sections that could answer a question, but still has something to do with the larger narrative. This modularity increases the likelihood that portions of your content will be selected for various related queries.

4. Address limitations of generative AI

Generative engines are not absolute. In fact, AI can often misinterpret language, especially if they are ambiguous, and even struggle with technical jargons. Sometimes they also lack access to the most recent information and conflate similar concepts or miss nuanced distinctions that are evident, especially to a trained eye. But understanding these limitations can also help you address them, giving you the edge to improve your contents. 

So how do you work around these issues?

• Be explicit rather than assuming context. Make sure that you provide a descriptive definition of terms when introducing them, even if you are talking to an audience that is familiar with the topic or terms. 
• Help the AI make accurate connections, giving your content the boost for the human readers to gather. 
• Update time-sensitive content regularly and include publication or update dates prominently. Where ambiguity exists, provide clarifying statements that help both humans and AI systems understand your precise meaning.

5. Measure success metrics 

Measuring success can help you confirm if your content strategies are working seamlessly, but since GEO is relatively new, there are companies that may be unaware of how to measure it. Here’s how:

• Audit existing content for AI visibility. This involves checking for clear structure, direct answers, updated data, strong contextual cues, and metadata that signals relevance and authority to LLMs.
• Track brand visibility. Monitor how often AI platforms mention or include your brand in generated responses using analytics tools that track conversational outputs, focusing on GEO-specific KPIs like AI response rate, brand citation frequency, and prompt inclusion rate.
• Measure conversion impact. Evaluate ROI through attribution models that account for AI-influenced touchpoints, using multi-touch attribution, AI-specific tracking links, and integrated frameworks that merge traditional analytics with AI response monitoring.

AnyBiz: the best sales tool to complement GEO