GEO vs SEO: How to Optimise for AI Search Engines

Search marketing is evolving—are you keeping up? Traditional SEO has been the foundation of online visibility for years, helping businesses rank in Google and drive organic traffic. However, as AI-driven search experiences gain traction, a new approach is emerging: Generative Engine Optimisation (GEO).

With AI tools like ChatGPT, AI Overviews, Bing Chat, and Perplexity AI changing how people search, marketing professionals must think beyond rankings. Brands need to adapt their strategies to stay visible in AI-powered search results.

So, what does this mean for SEO? Is GEO a replacement or an evolution? Let’s explore GEO vs SEO, their approach differences, and the best AI search engine strategies to future-proof your brand.

Estimated reading time: 24 minutes

What is Generative Engine Optimisation (GEO)?

Generative Engine Optimisation (GEO) is the process of ensuring your content is surfaced by AI-driven search tools like ChatGPT, Gemini, Bing Chat, Perplexity AI, and AI Overviews. Unlike traditional search engines that display a list of ranked links, AI search engines provide instant, conversational answers—pulling data from multiple sources rather than directing users to a single webpage.

But here’s where it gets tricky: AI models don’t just ‘crawl and rank’ content like Google does. Instead, they process vast amounts of data, prioritising credibility, context, and structure when selecting information to include in their responses.

That means businesses need a new optimisation strategy—one that ensures their content is AI-friendly, structured for machine interpretation, and positioned as a trusted source.

Key Differences between SEO and GEO

At the end of the day, GEO is just good, fundamental SEO done right. Both Search Engine Optimisation (SEO) and Generative Engine Optimisation (GEO) focus on visibility, but they work in fundamentally different ways.

Search Engine Optimisation:

• Focuses on improving a website’s ranking in search results, primarily on Google, to increase visibility.
• Relies on backlinks, keyword optimisation, structured data, and technical SEO to boost rankings.
• Drives relevant traffic and engagement by converting clicks from search results into meaningful on-site actions.

Generative Engine Optimisation:

• Focuses on getting included in AI-generated responses (e.g., AI Overviews, Perplexity AI).
• Prioritises structured content, credibility, and AI-friendly formatting to be recognised by AI models.
• Traffic is influenced by brand mentions, AI-generated citations, and authoritative sources used by AI tools.

Think of SEO as competing for Google’s top spots, while GEO is about ensuring AI-powered tools recognise and use your content in their responses. Rather than replacing SEO, GEO builds on SEO best practices and adapts them to how AI models process and present information.

It’s also worth mentioning that GEO shouldn’t be confused with geo-targeting (the practice of tailoring marketing efforts based on a user’s physical location).

Why Generative AI Is Changing How People Search

Search engines have always been about finding information, but AI-powered tools like ChatGPT, Perplexity AI, Claude, and AI Overviews are changing how that information is delivered. Instead of listing websites, these AI models summarise insights from multiple sources, presenting answers in a conversational way.

For businesses, visibility is no longer just about traditional search results. Whether customers actively use AI search tools like ChatGPT and Perplexity or passively encounter AI-generated content through features like AI Overviews, optimising for AI is essential.

Will GEO Replace SEO?

If AI search tools change how people find information, is SEO dead? Not at all.

As AI search becomes more widely utilised, many marketers are asking: “Will GEO replace SEO?” “Is SEO dead with AI?” “How is AI killing SEO?”

These questions reflect a general worry and confusion about the relationship between SEO and GEO and the continued relevance of traditional search optimisation.

The short answer? No—ChatGPT, AI Overviews, and other AI search engines will not replace SEO. Instead, they’re reshaping the way we think about visibility.

SEO remains essential for ranking in search engines like Google and Bing, but GEO is becoming important for ensuring your content is surfaced in AI-generated responses.

Rather than competing, the two work together. Modern SEO is GEO and GEO is SEO. Businesses that optimise for both increase their overall visibility, and chances of appearing in both organic search results and AI-curated answers.

A more realistic outlook? SEO and GEO aren’t separate skill sets—they’re two sides of the same coin. As AI-driven search becomes more sophisticated, marketing specialists will need to be proficient in both to create full-funnel digital strategies that maximise visibility across search engines and AI-generated platforms.

Should You Optimise for GEO? Why Generative Search Matters

By 2027, nearly 90 million people in the U.S. will be using generative AI first for online search.

While traditional SEO helps businesses rank in search engines, GEO ensures your brand is visible within AI-generated responses.

If you’re tempted to stick to a traditional SEO approach alone, consider this: optimising for AI helps with generative search, but it also boosts your presence in traditional search results.

“AIO keywords trigger 849% more Featured Snippets and 258% more Discussions compared to non-AIO queries.”

Louise Linehan, Ahrefs.

Recent research from Ahrefs found that AI-optimised keywords trigger 849% more Featured Snippets and 258% more Discussions than non-AI queries. That means businesses investing in GEO are also increasing their chances of appearing in Google’s valuable featured snippets—hitting two birds with one stone.

For industries where competition is fierce, the takeaway is clear: adjusting your approach with GEO in mind gives you a strategic advantage.

Take the travel industry, for example. In the past, searching “what to pack for camping with kids” on Google would return a list of blog articles and websites, often accompanied by People Also Ask and Featured Snippets. Today, many users see an AI Overview as the first result, summarising key information and linking to a limited selection of websites.

Search behaviour is even more dynamic for those using ChatGPT or Perplexity AI. Users can ask highly specific questions, such as “What are the top spots to camp in for a family of 5? Specifically in March on the North Island NZ?” Instead of scrolling through traditional search results, they receive a tailored response with sourced websites and personalised itineraries.

If your business isn’t optimised for generative AI, it may not appear in these AI-curated responses—limiting your visibility to potential customers.

So, how do you increase the likelihood of AI tools recognising and surfacing your content? It starts with understanding how these generative search engines work—and how to make them work for you.

How AI Search Engines Work

AI search engines are fundamentally changing how information is retrieved and presented. Unlike traditional search engines that rank and serve a list of links, AI-driven platforms like ChatGPT, Gemini, Perplexity AI, Bing Chat, and AI Overviews generate direct, conversational responses by analysing vast amounts of data.

These AI models don’t just match keywords or use static ranking factors. Instead, they focus on:

• Understanding Context: AI determines meaning rather than relying on exact keyword matches. Responses are built around how well content answers a query, not just where it ranks on a webpage. For example, AI Overviews responses have been found to appear most frequently for searches involving problem-solving or direct question-based queries, triggering in 74% of such cases (Search Engine Journal). This suggests that content designed to provide clear, structured answers to user problems is more likely to be included in AI-generated results.
• Pattern Recognition & Data Processing: These models predict the most useful information based on historical data, user intent, and available content.
• Credibility & Authority: AI prioritises reliable, well-cited sources, making brand reputation, citations, and expertise more valuable than ever.
• Content Structure & Accessibility: Well-organised, clearly formatted content is easier for AI to process and reference, making schema markup, structured headings, and concise writing essential for visibility.

But here’s where AI search engines differ significantly: they don’t just retrieve content—they learn from it.

Unlike traditional search engines that index pages and return them as ranked results, large language models (LLMs) ingest content to understand entities, relationships, and context. They then generate responses based on this understanding rather than simply surfacing original content verbatim.

Why This Matters for Content Visibility

• AI responses are a remix of data, not direct citations. While AI models may cite sources, they don’t return content exactly as written—they generate answers using insights gathered from multiple sources.
• Training data is evolving. Earlier AI models relied heavily on open-source datasets, but today’s LLMs train on a much broader mix of data sources, making content visibility in AI-driven search more complex.
• Content now serves a dual purpose. Instead of just ranking in traditional search results, content educates AI models so that brands and entities become relevant enough to be featured in AI-generated responses.

The Future of AI Search Visibility

For businesses, this means appearing in AI-generated search results requires more than just SEO best practices. Content must be structured, credible, and optimised for AI-driven search experiences.

Before exploring tailored approaches to AI models, let’s look at a brief evolution of AI search engines and what that means for digital visibility today.

A Brief History of AI in Search:

While AI-generated search engines feel like a recent innovation, the foundations were laid long before ChatGPT and AI Overviews.

The concept of machines processing and understanding information dates back to the mid-20th century. In 1950, Alan Turing, a pioneer of computer science, introduced the Turing Test—a benchmark for machine intelligence that laid the groundwork for AI as we know it today. While search engines wouldn’t emerge until decades later, this early work in machine learning and pattern recognition paved the way for modern AI-driven search models.

Since then, search technology has evolved through multiple breakthroughs. Here’s a brief selection of some of the most significant historical and current events shaping the way we search today:

• 1990s – The First Search Engines: Foundational search technologies (Archie, ALIWEB, WebCrawler, etc.) introduce early indexing and ranking methods.
• 1998 September – Google’s PageRank: Google introduces backlink-based ranking, transforming search quality.
• 2013 September – Hummingbird: Google shifts toward understanding intent rather than just matching keywords.
• 2015 October – RankBrain: Google’s first AI-powered search ranking system adapts results based on search context.
• 2018 October – BERT: Google enhances natural language understanding for more conversational queries.
• 2020 June – GPT-3: OpenAI’s GPT-3 demonstrates AI’s ability to generate human-like text at scale, laying the foundation for AI-driven search.
• 2022 August – Perplexity AI Launches: A search engine powered entirely by AI, providing real-time indexed answers.
• 2022 November – ChatGPT: OpenAI’s ChatGPT brings conversational AI to the mainstream, shifting how users engage with AI-generated search.
• 2023 February – Meta Releases Llama: Meta enters the AI space with its first large language model focused on open-source AI development.
• 2023 March – Google Bard (Gemini) Launches: Google launches Bard, its AI chatbot, later rebranded as Gemini, marking its entry into AI-driven search.
• 2023 March – Claude Launches as a Safety-Focused AI: Anthropic introduced Claude, an AI assistant that prioritises safety and ethical AI use. While not a major player in AI search, Claude reflects growing efforts to develop trustworthy AI models.
• 2023 May – Google SGE: Google experiments with AI-generated search results, a precursor to AI Overviews.
• 2023 May – Bing Chat: Microsoft integrates GPT-4 into Bing, making real-time AI-powered search widely available.
• 2023 July – Meta Launches Llama 2: Meta makes its AI model commercially available, challenging proprietary AI models like GPT-4.
• 2023 September – Mistral 7B: French AI startup Mistral AI releases Mistral 7B, a highly efficient open-weight language model that outperforms larger models like Llama 2 13B, advancing open-source AI search capabilities. In December 2023, Mistral AI introduced Mixtral 8x7B, solidifying Mistral’s position as a major player in efficient AI-driven search technologies.
• 2024 May – AI Overviews Rollout: Google transitions from SGE to AI-generated search results, transforming search visibility.
• 2024 July – SearchGPT Beta: OpenAI tests SearchGPT Beta, exploring AI-powered search. While not yet a full competitor to Google or Perplexity, it signals OpenAI’s move into AI-driven search.
• 2024 December – ChatGPT Search Launches: OpenAI officially introduced ChatGPT Search, allowing all ChatGPT users to access real-time search results with AI-generated responses. This marked a shift in ChatGPT’s capabilities, moving it closer to an AI-powered search engine alternative.
• 2025 January – Perplexity AI Integrates Tripadvisor Data: AI search engines begin prioritising structured, authoritative sources over traditional rankings.
• 2025 January – DeepSeek R1 Launches, Disrupting the AI Market: DeepSeek launches its advanced multimodal LLMs, DeepSeek-R1 and DeepSeek-R1-Zero. These models achieve performance comparable to leading Western models but at a fraction of the cost, positioning DeepSeek as a competitor to OpenAI.

Each of these milestones has made AI-driven search more sophisticated, intuitive, and essential for businesses looking to maintain online visibility.

This is just a brief list of some of the most significant moments in search so far—countless innovations have shaped how we find and retrieve information today, and more will come.

With AI search now a dominant force, the next step is understanding how different AI models retrieve, process, and prioritise information.

Understanding Different Generative AI Models

Not all generative AI search engines work the same way—so optimising for them requires a tailored approach.

AI-powered search engines like ChatGPT, Gemini, Perplexity AI, Claude, SearchGPT, and Llama each have unique ways of processing, retrieving, and presenting information. Some are designed for concise, factual answers, while others specialise in conversational responses, creative generation, or deep contextual analysis. Understanding these differences is key to optimising your content for the right AI engines and audience.

Additionally, real-time web accessibility varies across AI models, influencing how they retrieve and present information:

• Perplexity AI & AI Overviews: These tools excel at summarising information from multiple sources, making structured, well-cited content more likely to be featured.
• ChatGPT & Claude: These models primarily rely on pre-trained data with a fixed knowledge cutoff and do not have built-in real-time web access. However, users can enable web browsing for searches. For these models, comprehensive, well-structured content that demonstrates authority and prioritises conversational, contextual understanding works best.
• Gemini & Bing Chat: These models are designed with built-in real-time web access, meaning that fresh, regularly updated information is crucial for visibility in their responses.

While a strong, general GEO strategy can improve visibility across AI search engines, tailoring certain elements to how different models retrieve, prioritise, and present content can further enhance discoverability.

Three Types of AI Engines: Training-Based, Hybrid, and Real-Time

AI search engines generally fall into three categories: training based, hybrid, and real-time models. Keep in mind, not all AI search engines generate content in exactly the same way due to their LLM parameters.

1. Training-Based AI

These models rely on pre-trained datasets with a fixed knowledge cutoff, meaning they do not continuously retrieve live data and typically reference content available before their last update.

Examples: Older versions of ChatGPT (e.g., GPT-3.5) and Claude fall into this category.

Most likely to feature: Established, widely cited, and evergreen content—such as foundational industry knowledge, well-documented best practices, and general reference material. Since these models do not update dynamically, newer content will only be included in future iterations when training data is refreshed.

Can ChatGPT and Claude Access Live Data?

Yes—but only in specific cases when web browsing is enabled or when a user manually inputs a link for the model to read. However, this does not make them real-time AI search engines because:

1. Browsing is not always active and must be enabled by the user.
2. They do not continuously update their knowledge base with new content like AI search engines such as Perplexity AI or AI Overviews.
3. By default, responses are still generated from pre-trained datasets, not live web searches.

Optimisation Tip: While it’s not practical to optimise for outdated AI models, structuring content to be authoritative, evergreen, and well-linked increases its chances of being referenced in future iterations of AI models when their training datasets are updated.

Note: Since training-based AI does not retrieve live data by default, its impact on search visibility is tied to when its dataset was last updated. Businesses should focus on optimising for real-time and hybrid AI search engines, where ongoing content updates have a direct impact.

2. Hybrid AI

Hybrid AI models combine pre-trained data with some level of real-time updates. They may retrieve live information selectively, depending on the model and the query.

Examples: ChatGPT with web browsing enabled, Claude, Bing Chat, Mistral AI, and Gemini.

Most likely to feature: Content that balances foundational knowledge with ongoing updates—such as industry trends, evolving best practices, and educational resources that require periodic revisions.

Optimisation Tip: Create a mix of evergreen content and fresh updates. Use structured data (schema markup) to highlight recent information while ensuring your core content remains strong.

3. Real-Time AI

Real-time AI engines actively fetch live data from the internet, ensuring responses reflect the most current information available.

Examples: Perplexity AI, AI Overviews, Bing Chat, and DeepSeek.

Most likely to feature: Frequently updated, time-sensitive content—especially valuable for industries where real-time accuracy is critical, such as news, finance, e-commerce, and technology.

Optimisation Tip: Implement dynamic content strategies, such as API integrations, frequent updates, and fresh blog posts, to maintain AI visibility.

Why This Matters for GEO

Optimising for AI search engines requires understanding how AI models retrieve, process, and present content. Each platform prioritises information differently, making structured, authoritative, and AI-friendly content essential—especially for real-time and hybrid models that continuously index fresh data.

But visibility in AI search isn’t just about how AI retrieves content—it also depends on search intent. Different models cater to different stages of the user journey, from informational queries to high-intent transactional searches.

Aligning your content with how AI search engines interpret and serve user intent will increase the chance of your content surfacing and reaching customers via AI search.

AI Search Engines and the Conversion Funnel

Not all AI search engines return the same types of results. Some prioritise broad, research-focused content, while others are better for evaluating products or guiding purchase decisions. This breakdown maps AI search behaviour to different intent stages, helping you optimise your content to engage users at the right point in their journey.

1. Informational Intent (Awareness Stage):
   • Purpose: Users seek knowledge or answers to specific questions without immediate intent to purchase. Queries often start with “how,” “what,” or “why.”
   • AI Engines Optimised for This Stage: Models like ChatGPT and Claude excel in providing detailed explanations and comprehensive information, making them suitable for addressing informational queries.
   • Content Strategy: Develop in-depth articles, guides, and tutorials that answer common questions in your industry. This positions your brand as an authority and attracts users in the early research phase.
2. Commercial Intent (Consideration Stage):
   • Purpose: Users compare products or services, seeking reviews or comparisons to inform their purchasing decisions.
   • AI Engines Optimised for This Stage: Perplexity AI and Bing Chat aggregate information from multiple sources, offering summaries and comparisons that aid users in the evaluation process.
   • Content Strategy: Create comparison articles, product reviews, and case studies that highlight the benefits and features of your offerings compared to competitors.
3. Transactional Intent (Conversion Stage):
   • Purpose: Users are ready to make a purchase or complete a specific action, often using queries with terms like “buy,” “discount,” or “pricing.”
   • AI Engines Optimised for This Stage: While traditional search engines have been the primary avenue for transactional queries, AI models are evolving to assist in this area. For instance, Bing Chat integrates with e-commerce platforms to facilitate purchases directly through conversational interactions.
   • Content Strategy: Ensure your product pages are optimised with clear calls-to-action, up-to-date pricing, and easy navigation. Incorporate transactional keywords to align with purchase-ready queries.

Key Takeaways

Understanding how AI search engines align with different stages of the conversion funnel allows you to optimise content for user intent, increasing engagement and the likelihood of conversion. By structuring content to match AI search behaviour, businesses can guide users from awareness to action more effectively.

Optimising for GEO: How to Stay Visible in AI Search

“In the end, the companies that win will be the ones that do authentic content that people want to engage with, that actually gives value.”

Richard Conway – Source: RNZ – What Businesses Need to Do to Get in Front of AI Searches

The truth is, there’s no definitive playbook—yet.

This guide breaks down the differences between SEO and GEO, helping you understand how AI search engines rank content differently from traditional search. However, the two approaches aren’t entirely separate—many SEO best practices, such as following E-E-A-T principles (Experience, Expertise, Authoritativeness, and Trustworthiness), also benefit GEO.

To maximise visibility in AI-driven search results, businesses should focus on four key areas:

1. Research & Analysis: Understanding AI-Generated Responses

Generative AI connects the dots by interpreting context, recognising entities, and understanding relationships between people, places, and brands. To optimise for AI-driven search, businesses must first analyse how AI engines retrieve and present content.

Action Steps:

• Use Ahrefs’ AI Overviews ranking tool to check how your existing content appears in AI-generated search results and identify optimisation opportunities.
• Go directly to Google and analyse how AI Overviews select and display sources to understand what factors influence ranking. Do the same for other search engines, too.
• Test different phrasing and structuring techniques in ChatGPT, Perplexity AI, Bing Chat, and AI Overviews to see what AI prioritises.

Research competitor content that appears in AI-generated responses to identify patterns and gaps you can leverage.

2. Content Optimisation: Structuring for AI-Friendly Results

Generative AI engines don’t rank content like traditional search engines. Instead, they:

• Prefer well-structured, clearly formatted content over dense, unformatted text. AI can process long-form content but extracts information more effectively from structured sections.
• Surface question-based content that aligns with common user queries, making FAQs and direct answers highly valuable.
• Prioritise fact-based, well-cited sources to ensure accuracy and reliability in AI-generated responses.

Actionable Step:

• Use FAQs, bullet points, and schema markup to make content easy for AI to retrieve.
• Structure long-form content with clear headings, concise summaries, and well-defined sections to improve AI readability and citation potential.
• Ensure content directly answers key questions in an AI-friendly format, increasing the likelihood of being referenced.

3. Influencing AI: From Keyword Optimisation to Brand Authority

“The game has shifted from link-building to voice-building.

AI doesn’t need hyperlinks to understand authority, it reads context like humans do. Your digital footprint is now measured by who’s talking about you, not just linking to you.

The secret? Being genuinely quotable beats being technically optimized. While everyone is creating AI slop, the winners will be those brave enough to stand out with original insights, unique opinions and bold perspectives.

This isn’t just a shift in search; it’s a return to what actually matters: saying something worth repeating!”

Britney Muller (AI Consultant, SEO Expert, Data Science Obsessed)

AI search engines prioritise credibility and trustworthiness. While well-structured content is essential, AI engines also assess brand authority, citations, and reputation when selecting sources for AI-generated responses.

To increase the likelihood of appearing in AI-driven search results:

• Build your brand voice: Maintain consistent brand mentions across trusted platforms. How? As AI and SEO expert, Britney Muller says, by being genuinely quotable and saying something worth repeating! Get your brand and voice referenced in news outlets, industry websites, and authoritative sources.
• Leverage PR to align your brand with key industry topics—getting mentioned alongside relevant keywords ensures AI models associate your brand with important discussions. Optimise author bios with targeted keywords and credibility signals to reinforce expertise.
• Produce authentic data-backed content that AI models can trust and reference. This includes expert-led insights, unique research, and proprietary datasets that provide fresh information AI can’t find elsewhere.
• Increase your thought leadership authority by participating in expert commentary, media coverage, and industry discussions, ensuring AI recognises your voice in key subject areas.

“AI search isn’t just about keywords anymore—it’s about brand adjacency. It’s looking at how often your brand is talked about in the right context.

If people consistently mention your brand when discussing running, fitness, or footwear, AI will start recognising you as relevant—way beyond just ranking for ‘best running shoes’.”

Peter Mangin – Source: AI Innovisory

4. Technical Foundations: Ensuring AI Can Access & Understand Your Content

Just like traditional search engines, AI search engines prioritise structured, accessible, and up-to-date content to ensure accurate retrieval and presentation of AI-generated results. Businesses need to optimise their technical setup so AI can efficiently process and reference their content.

Key areas of optimisation include:

• Schema Markup: Structured data (e.g., FAQ schema, product schema) helps AI interpret content context, such as a restaurant’s menu, hours, and reviews, via schema.org.
• Headings & Formatting: Clear, descriptive headings improve AI comprehension and align with common user queries (e.g., “How to Choose the Right Financial Planner”).
• Page Speed & Mobile Optimisation: Faster, mobile-friendly pages improve user experience and AI accessibility.
• Metadata & Internal Linking: Well-optimised meta titles, descriptions, and internal links enhance discoverability.
• Content Accessibility: AI models favour websites that comply with accessibility standards (e.g., WCAG guidelines) for inclusive and user-friendly content.
• Regular Updates: Frequently refreshed pages signal relevance and accuracy, making AI engines more likely to retrieve your content.
• API Integrations for Dynamic Content: Real-time data feeds (e.g., finance, e-commerce) ensure AI references the latest information rather than outdated content.

Actionable Step:

✔ Use Google PageSpeed Insights to improve load times and optimise mobile experience.

✔ Implement schema markup to structure content for AI readability.

✔ Regularly audit and update metadata, internal links, and key content to enhance AI discoverability.

What Kind of Content Gets CTR in AI?

While AI-generated answers often provide instant responses, people still click through when they need more depth.

If AI provides an answer that doesn’t sate user queries, people are likelier to click through to the linked website for deeper insights, more data, opinions, or up-to-date trends.

If you want to influence users to see your brand content in AI search and click through, create more future-oriented thought leadership pieces. AI is excellent at providing step-by-step instructions and answering “how-to” queries, but not necessarily future trends, expert predictions, and unique insights.

With nearly 60% of searches ending in zero clicks (Sparktoro), brands must focus on original insights, expert opinions, and fresh data to encourage engagement and click-throughs.

Tracking Success in AI Search

Businesses need to track how AI models retrieve, interpret, and present their content to measure success in AI search. Here are key performance indicators for tracking success.

1. How often does AI reference your content?

Measure how frequently AI search engines include your content in responses.

How to track:

• Test relevant queries in ChatGPT, Perplexity AI, Gemini, and Bing Chat to check if your content appears.
• Monitor Google AI Overviews to see if your site is cited.
• Optimise content structure to align with patterns in AI retrieval.

2. How accurately does AI represent your brand?

AI-generated responses should present your brand and information correctly. Inaccurate summaries or misrepresentations can mislead users.

How to track:

• Audit AI-generated responses that mention your brand.
• Identify inaccuracies and update your content with clear, structured data.
• Use schema markup and authoritative sources to reinforce accuracy.

3. How do users engage with AI-generated mentions of your brand?

Click-through rates (CTR) and engagement levels show whether AI-driven search leads users to your content or brand interactions.

How to track:

• Use Google Analytics, UTM tracking, and referral reports to measure AI-driven traffic.
• Monitor brand mentions in AI-generated discussions on forums and Q&A platforms.
• Assess how often AI chatbots reference or suggest your content in response to user queries.

4. Is your brand visible for high-value queries?

Some generated responses are more valuable than others. For example, appearing in commercial or industry-relevant searches is great for brand authority.

How to track:

• Identify key industry and commercial queries relevant to your business.
• Test these queries in AI search tools to see if your content surfaces.
• Optimise pages for structured answers, authoritative citations, and brand mentions.

5. Are you getting AI-driven leads?

While AI models may not drive direct website traffic, they influence purchasing decisions through recommendations and citations.

How to track:

• Use Google Analytics and CRM tools to track form fills, downloads, or purchases attributed to AI-driven interactions.
• Map AI-assisted customer journeys to understand engagement before conversion.
• Apply multi-touch attribution models to measure how AI search contributes to conversions.

6. Are there any gaps in AI responses?

AI-generated answers aren’t always complete. Finding where AI lacks information allows businesses to position themselves as a trusted source.

How to track:

• Analyse AI-generated responses for missing or incomplete answers.
• Identify topics where your brand should be referenced but isn’t.
• Create targeted, structured content to fill these gaps and improve AI recognition.

What’s Next? Building a Future-Ready Strategy for AI Search

SEO and GEO aren’t separate strategies—they are two sides of the same coin. As AI-driven search becomes more prominent, businesses need a combined approach that optimises for both search rankings and AI-generated visibility.

At Pure SEO, we help businesses stay ahead of these changes. Whether creating AI-friendly content, implementing technical strategies, or creating a full-funnel digital marketing strategy, we work with our clients as partners to develop tailored strategies that work for them.

Want to future-proof your brand’s visibility in AI search? Get in touch—we’d love to help.