AI SEO vs Traditional SEO: Key differences and why both matter

For years, traditional SEO was primarily focused on improving Google search rankings, driving organic traffic, and generating leads from people clicking through to a company's website. Businesses researched relevant keywords, optimized their pages, built backlinks, and tracked how these activities contributed to rankings, traffic, and conversions.

Those activities remain important, but the way people discover information through search is changing.

Today, someone looking for CRM software might 

• Search Google and receive an AI Overview comparing different platforms. 
• Another person might ask ChatGPT or Perplexity to recommend CRM software for a specific business requirement.

In both cases, the user can receive product recommendations and supporting information without necessarily visiting the websites of the companies being discussed.

AI SEO focuses on improving a brand's visibility across these AI-generated answers, whether users encounter them through Google Search or an LLM platform.

In our research, How Big Is AI Search?, we estimated the number of searches happening across traditional search engines and leading LLM platforms using Q2 2026 data. We found that traditional search engines generated approximately 535.2 billion monthly searches, compared with 45.9 billion search-equivalent queries across LLMs.

Traditional search still accounts for more than nine out of ten search-equivalent queries. But Google is also introducing more AI-generated answers directly within its search results. This means businesses need to consider how they appear in traditional search listings and AI-generated responses.

In this article, we'll examine the differences between traditional SEO and AI SEO, why the existing SEO approach needs to change, and how businesses can build a strategy that improves visibility across both Google and LLM platforms.

What is traditional SEO?

Traditional SEO is the process of optimizing a website to improve its visibility and rankings in organic search results on search engines such as Google and Bing.

The main objective is to attract relevant visitors to a website through organic search and convert those visitors into leads or customers.

For example, a company offering CRM software might want to rank on Google for keywords such as:

• Best CRM software for startups
• CRM software for small businesses
• Sales CRM platforms
• CRM software comparison

When someone searches for one of these keywords, the company wants its website to appear among the top organic search results. A higher ranking can increase the likelihood of people discovering the website and clicking through to learn more about the product.

Traditional SEO generally involves several activities:

• Keyword research helps identify the search terms potential customers use, how frequently those terms are searched, and what they want to accomplish.
• On-page SEO involves optimizing page content, headings, title tags, internal links, and other elements to improve relevance for target search queries.
• Technical SEO helps search engines crawl, render, and index website pages. This includes maintaining a suitable website structure, managing indexing, improving page experience, and resolving technical issues.
• Off-page SEO focuses on building authority through relevant backlinks, digital PR, and references from other websites.

Businesses typically measure the success of these activities through keyword rankings, organic impressions, clicks, website traffic, leads, and conversions.

These practices are still relevant today. What has changed is that search visibility can no longer be evaluated through organic rankings and website visits alone.

What is AI SEO?

AI SEO is the process of improving a brand's visibility in AI-generated answers across traditional search engines and LLM platforms such as ChatGPT, Gemini, Claude, and Perplexity.

The objective is to increase the chances of a company being mentioned, cited, or recommended when users search for information related to its products or services.

For example, consider someone asking ChatGPT:

"What are the best CRM platforms for a B2B SaaS company with a 20-person sales team?"

The AI assistant might recommend several CRM platforms, explain their capabilities, and provide links to websites or articles supporting its recommendations. For a CRM software company, appearing in that response is a form of AI search visibility.

There are three main outcomes businesses can track.

These outcomes can influence how potential customers discover and evaluate software products.

AI SEO is also commonly referred to as Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO). Although the terms are sometimes used differently, they broadly refer to improving the likelihood of content or brands appearing in AI-generated answers.

At ScaleTheory, we consider AI SEO an extension of SEO.

Our approach involves continuing to improve search rankings while also understanding how brands appear in AI-generated answers, which sources are cited, and what can be done to increase visibility for commercially relevant topics.

Is your brand visible across Google and AI search?

Your buyers are researching solutions on Google, ChatGPT, Perplexity, and other AI platforms. Find out where your brand appears, which competitors are being recommended, and which search opportunities you're missing.

Audit Your AI Visibility

AI SEO vs traditional SEO: 6 key differences

Traditional SEO and AI SEO share several underlying practices, including content creation, technical optimization, and authority building.

The main differences are in where businesses want visibility, how they research opportunities, what they optimize, and how they measure results.

Here's a comparison:

Let's examine how these differences affect SEO strategy and execution.

1. Search rankings and website clicks vs AI mentions, citations, and recommendations

Traditional SEO focuses on improving the position of a website in search results for relevant keywords.

For example, if a CRM company ranks in the top three Google results for "best CRM software for startups," it has an opportunity to attract visitors searching for that solution. The business can track the keyword's ranking position, impressions, clicks, and conversions.

AI SEO introduces another form of visibility.

When someone asks ChatGPT to recommend CRM platforms for startups, the response may include several vendors and explain why each one is suitable. A company may appear in that recommendation even if the user doesn't visit its website.

Similarly, an AI Overview on Google may summarize the available options and cite several supporting websites.

The difference is that traditional SEO primarily measures visibility through search rankings and traffic, while AI SEO also measures whether a brand appears within the answer itself.

A website can perform well in traditional search without being mentioned in an AI-generated response for the same topic. The opposite can also happen.

Businesses therefore need to track both outcomes rather than assuming that strong Google rankings automatically mean strong AI visibility.

2. Keyword research vs conversational and long-tail prompts

Traditional SEO typically begins with identifying keywords that potential customers search for on Google. SEO teams evaluate these keywords based on search volume, competition, relevance, and the user's intent.

For example, a CRM software company might target the keyword "CRM software for startups." AI search introduces more variations in how users ask questions. Instead of entering a short keyword, someone might ask:

"Which CRM software would be suitable for a 15-person B2B SaaS sales team that needs HubSpot integration and automated lead tracking?"

Both queries relate to CRM software, but the second includes more information about the user's requirements. AI systems can use that additional context when generating recommendations.

Some AI search experiences also use query fan-out.

For example, Google explains that AI Overviews and AI Mode can issue multiple related searches to gather information before generating an answer. A prompt about CRM software might involve related searches about pricing, integrations, product comparisons, and specific use cases.

This changes how businesses should conduct search research:

• For traditional SEO, you still need to understand keyword search volume and search intent.
• For AI SEO, you also need to identify the questions buyers might ask while researching or comparing solutions.

Both should start from the same commercial topics relevant to your business. The difference is how those topics are researched across Google keywords and conversational AI prompts.

3. Keyword-optimized content vs answer-first, structured content

Traditional on-page SEO involves optimizing content around relevant keywords and search intent. This includes writing useful articles, structuring headings, optimizing title tags, and linking to related pages.

AI SEO builds on these practices by placing additional attention on how clearly content answers specific questions.

For example, consider a section explaining whether a CRM platform supports automated lead assignment. A generic explanation might spend several paragraphs discussing why lead management matters before explaining what the product can do.

A more direct approach would begin with:

"Our CRM supports automated lead assignment based on territory, deal size, and predefined routing rules."

The section can then explain how the feature works, which plans include it, and any limitations. This makes the answer easier to understand without requiring readers to work through unrelated information.

For AI SEO, the main content considerations include:

• Answering the user's question directly before providing additional context.
• Using descriptive headings that clearly identify what each section explains.
• Including comparison tables, FAQs, and examples where they help readers.
• Supporting claims with product documentation, research, or customer evidence.
• Writing sections that make sense even when read independently.

Relevant schema markup can also describe products, organizations, articles, and other information in a machine-readable format.

However, structured data isn't a guaranteed method of earning AI citations. Google's documentation confirms that no special AI-specific markup is required for appearing in AI Overviews or AI Mode.

The priority should remain on creating content that answers relevant questions accurately and provides useful supporting information.

Good content structure can support both traditional rankings and AI search visibility.

4. Backlinks vs brand mentions and third-party authority

Backlinks have been an important part of traditional SEO because they help search engines discover pages and assess their authority.

For example, if several established marketing publications link to a CRM company's research article, those links can contribute to the website's authority and search performance.

AI search adds another consideration.

When generating recommendations, AI systems may retrieve information from third-party websites such as industry publications, review platforms, comparison articles, and community discussions. These sources can describe a company even when they don't link directly to its website.

For example, suppose an independent publication publishes an article comparing the best CRM software for startups. The article includes your product, explains its features, and compares it with alternatives.

If an AI search platform cites that article when answering a related question, the information could contribute to how your brand is presented.

The exact influence depends on the AI platform and the sources it retrieves. An unlinked mention is not automatically a ranking signal or a guarantee of recommendation.

This introduces a practical difference in off-page strategy:

• For traditional SEO, you might prioritize earning relevant backlinks to improve the ranking potential of specific pages.
• For AI SEO, you should also identify the third-party websites frequently cited for your commercial topics and look for legitimate opportunities to build brand presence on those sources.

These activities can overlap. An industry article might provide a valuable backlink while also introducing your company to potential customers and AI search systems.

The goal is to improve authority and visibility across both search experiences.

5. Traditional technical SEO vs AI search crawlability

Traditional technical SEO focuses on helping search engines access, render, index, and understand website content.

Common activities include maintaining crawlability, improving website structure, fixing indexing issues, managing redirects, and ensuring that important information is accessible.

These fundamentals remain relevant for AI search:

• Google's AI Overviews and AI Mode use Google's existing Search infrastructure, so businesses still need to follow established SEO practices.
• Other AI platforms may use separate web crawlers or retrieval systems.

For example, OpenAI distinguishes between OAI-SearchBot, which supports ChatGPT search discovery, and GPTBot, which is associated with model training.

A business can manage access for these crawlers separately through robots.txt. This distinction matters because allowing a crawler used for search discovery isn't necessarily the same decision as allowing content to be collected for model training.

AI SEO technical checks may therefore include:

• Reviewing relevant AI crawler permissions.
• Making sure important content can be retrieved without relying on inaccessible page elements.
• Continuing to implement appropriate structured data.
• Keeping product information accurate and up to date.

There is no need to rebuild the entire technical SEO process for AI search. Instead, extend existing technical audits to account for the additional platforms through which content may be discovered.

6. Organic search results vs AI-generated answers in the buyer journey

Traditional search often involves users moving between search results and websites to gather information.

Consider someone researching CRM software.

They might search Google, open several vendor websites, review product features, read comparison articles, and eventually shortlist a few options.

AI search can bring several parts of that research process into one conversation.

For example, a user might ask ChatGPT to recommend CRM software for a startup, then follow up with questions about pricing, integrations, limitations, and alternatives.

The assistant can respond to each question while retaining context from the earlier conversation. This allows potential customers to gather information and narrow down their options before visiting a vendor's website.

For businesses, this changes what visibility can mean.

A brand might influence a buyer's shortlist through an AI recommendation without generating an immediate website visit. At the same time, users may still visit product websites when they need detailed information, demonstrations, or direct contact with vendors.

Traditional SEO remains important for generating website traffic, while AI SEO expands the opportunities to influence buyers during their research and evaluation.

This is also why website traffic alone may not capture every interaction that contributes to a purchasing decision.

"

ScaleTheory helped us bring structure to organic growth at Gloroots and focused on the areas that would create the most impact on pipeline, instead of just driving traffic. The team moved fast, prioritized the right opportunities, and stayed closely aligned with our business goals throughout the engagement.

Mayank

Co-founder, Gloroots

Is AI SEO replacing traditional SEO?

AI SEO is changing traditional SEO, but traditional search engines are not being replaced by LLMs in terms of search volume.

The distinction matters because two different changes are happening at the same time:

1. The growth of LLM platforms such as ChatGPT, Gemini, Claude, and Perplexity.
2. The increasing presence of AI-generated answers within Google Search itself.

Many discussions about AI replacing SEO focus almost entirely on the first. But our research suggests that the second deserves at least as much attention.

Traditional search still accounts for more than 90% of search demand

In our research, How Big Is AI Search?, we estimated that traditional search engines generated 535.2 billion searches per month in Q2 2026.

LLM platforms generated approximately 88.9 billion monthly prompts, but not all those prompts were searches. Some involved writing, coding, summarization, or other tasks that aren't directly comparable to search engine queries.

After adjusting for prompts considered search-equivalent, we estimated approximately 45.9 billion monthly LLM search queries. This puts LLMs at approximately 7.9% of global search-equivalent demand, compared with 92.1% for traditional search engines.

The numbers suggest that people are using AI assistants at scale, but the growth of these platforms hasn't eliminated traditional search demand.

Google and other search engines continue to account for most search activity. So businesses shouldn't abandon traditional SEO based on the assumption that users have stopped searching on Google.

Google itself is becoming an AI search engine

Google now presents AI-generated answers through AI Overviews and AI Mode alongside its traditional search features. And the frequency of these answers is increasing.

In October 2026, Ahrefs published an analysis of 232.8 million US desktop Google search-result crawls collected between July and September.

The September averages showed:

• 73.3% of branded keywords in the dataset triggered AI Overviews.
• 78.3% of non-branded keywords in the dataset triggered AI Overviews.

These findings describe the US desktop keyword sample analyzed by Ahrefs. They don't establish that the same percentage of all Google searches worldwide produces an AI Overview. However, they show how extensively AI-generated answers have appeared within the search results being monitored.

We examined the potential scale of this change in another ScaleTheory research article, Google Has 7–8X More AI Exposure Than LLMs. Our analysis compared estimated Google searches that could trigger AI Overviews with search-equivalent queries taking place across leading LLMs.

Using an estimated 471.9 billion monthly Google searches and an assumed AI Overview presence of 70–80%, we estimated approximately 330–378 billion potential AI Overview-triggering searches per month.

This compares with 45.9 billion estimated search-equivalent LLM queries.

Based on those assumptions, Google offers approximately 7–8 times more potential AI-answer exposure than LLM search.

This is an estimate of potential exposure, not a measured count of AI Overview views, brand mentions, or citations. Applying the observed keyword coverage to overall Google search volume is also an assumption, rather than a directly measured global result.

Still, the comparison provides an important perspective. AI-generated answers are becoming more common within the search engine that already handles the majority of search demand.

For businesses, this means AI SEO cannot be limited to improving visibility in ChatGPT and other LLM platforms. Google AI Overviews and AI Mode also need to be considered.

Search rankings no longer explain the complete visibility picture

Another change is how users interact with search results.

A high Google ranking can still generate traffic, but an AI-generated answer may provide enough information for the user to continue without visiting a website.

Several studies have examined this behavior.

• SparkToro's June 2026 research, based on Similarweb clickstream data, found that 68.01% of US Google searches ended without a click during the first four months of 2026. This includes zero-click activity caused by several factors, not only AI Overviews.
• Ahrefs found that the presence of an AI Overview correlated with a 58% lower average click-through rate for the top-ranking page in its December 2025 dataset.
• Pew Research Center found that users clicked traditional search results in 8% of visits where an AI summary appeared, compared with 15% when no AI summary appeared.

These studies use different methodologies and shouldn't be treated as interchangeable measurements. But they point toward a practical problem for SEO teams.

A business can maintain its search ranking while receiving fewer clicks, particularly when Google provides an AI-generated answer directly on the results page.

Businesses should continue improving rankings and organic traffic, but also measure whether their brands are mentioned, cited, and recommended in AI-generated responses.

They also need to consider other indicators of demand, including branded searches, direct traffic, and leads that report discovering the company through AI. These measurements won't provide perfect attribution, but they can help explain changes that traditional ranking reports might miss.

The underlying SEO activities remain relevant. What is changing is how success should be evaluated and where a company needs visibility.

Google Has 7–8X More AI Exposure Than LLMs

Our research estimates that 330–378 billion Google searches every month could trigger AI Overviews, compared with 45.9 billion search-equivalent queries across LLMs. See the data behind why Google should be a priority in your AI search strategy.

Read the full research →

How to combine traditional SEO and AI SEO in one strategy

For most B2B SaaS companies, managing traditional SEO and AI SEO as two completely separate activities would create unnecessary duplication.

The same products, buyer requirements, and commercial topics matter across Google and LLM platforms. The difference is how you identify opportunities and use the available data to prioritize content and authority-building activities.

We recommend starting with the topics your company wants to own and analyzing them from both perspectives before deciding what content to create or optimize.

Step 1. Identify your core commercial topics and audit your current visibility

Start by identifying the topics most closely connected to your product and your ideal customer profile (ICP). These are the subjects potential customers are likely to research when they need a solution like yours.

For a CRM software company, the priority topics might include:

• CRM software for startups
• Sales pipeline management
• CRM automation
• CRM software integrations
• CRM software comparisons

These topics should form the basis of your strategy across both traditional search and AI platforms.

1. Audit your current Google search performance

Next, assess your current visibility for each topic.

On Google, identify the keywords where your website already ranks, your current ranking positions, and the pages receiving organic traffic.

You can use Google Search Console and traditional SEO platforms such as Ahrefs or Semrush to understand your existing performance.

2. Benchmark your visibility across AI platforms

For AI search, create relevant buyer prompts around the same topics and track them across ChatGPT, Gemini, Perplexity, and other platforms relevant to your audience.

For example, under the topic "CRM software for startups," you might track:

1. What are the best CRM platforms for early-stage SaaS startups?
2. Which CRM software is suitable for a 15-person sales team?
3. What are the best alternatives to HubSpot for a small B2B company?
4. Which CRM tools offer sales automation and lead tracking?

Use an AI visibility monitoring tool to record which brands appear in the answers, which competitors are recommended, and which websites are cited.

This provides two views of your current visibility:

• Where your website ranks on Google.
• Where your brand appears in AI-generated recommendations.

You can then identify the topics where you're performing well, where competitors are ahead, and where you have little visibility.

Step 2. Target relevant SEO keywords and AI search prompts based on buyer intent

Once you've identified your commercial topics, the next step is to map the keywords and AI prompts associated with each topic. The objective is to understand how buyers search for the same solution across different platforms.

1. Identify relevant keywords for traditional SEO

For traditional SEO, use keyword research to identify search volume, keyword difficulty, existing rankings, and search intent.

For example, the topic "CRM software" might include keywords such as:

• CRM software for startups
• Best sales CRM
• CRM software comparison
• CRM with email automation

These keywords can guide which pages you need to create or optimize for Google.

2. Create relevant buyer prompts for AI search

For AI SEO, identify conversational prompts that potential customers might use when evaluating the same category.

Examples include:

• Which CRM software would work best for a SaaS startup with a small sales team?
• What are the best CRM platforms with built-in email automation?
• Which CRM tools are affordable alternatives to Salesforce?

The prompts should reflect actual buyer requirements rather than simply converting every keyword into a longer question.

You can gather these questions from sales conversations, customer interviews, product reviews, existing search queries, and competitor research. Then add them to your AI visibility tracking tool. This allows you to monitor how your brand appears for the topics that matter to your business.

One difference to remember is that Google keyword search volume and AI prompt visibility aren't directly comparable.

Traditional SEO tools estimate how frequently people search for specific keywords. AI visibility tools generally measure how brands appear across selected prompts and sampled AI responses.

Use these datasets together to understand opportunities, but don't treat prompt mention frequency as a direct equivalent of keyword search volume.

Step 3. Optimize content using both Google search results and AI citation data

This is where the research from both channels comes together.

Suppose you've decided to create or improve a page targeting the topic "CRM software for startups."

Traditionally, you would analyze the top-ranking Google pages for relevant keywords. You might review their content structure, topics covered, search intent, supporting evidence, and the information they provide.

For AI SEO, you should also examine the pages that AI platforms frequently cite when answering prompts about CRM software for startups.

1. Compare top-ranking Google pages and frequently cited AI sources

The idea is to analyze two groups of pages:

1. Pages ranking highly on Google for your target keywords.
2. Pages frequently cited in AI-generated answers for relevant buyer prompts.

Some pages may appear in both groups, while others may be cited by AI platforms despite having different positions in traditional search results.

Looking at both provides a clearer understanding of what content is visible across these search experiences.

For example, suppose a Google search for "best CRM software for startups" returns several software comparison articles. At the same time, your AI visibility tracking tool shows that ChatGPT and Perplexity repeatedly cite a different set of comparison pages.

You can review both sets to understand:

• Which questions the pages answer.
• What product comparisons and use cases they cover.
• Whether they provide original data or customer evidence.
• How they structure explanations and comparisons.
• What information may be missing or outdated.

These findings can help you understand why certain pages are performing well across Google search results and AI citations.

2. Decide whether to create new content or improve existing pages

Use those findings to decide whether to create a new page or improve an existing one.

There are two possible scenarios:

Scenario 1: You don't have a relevant page covering the topic.

If you don't have a relevant page, create content that covers the buyer's question properly and provides useful information beyond what existing sources offer.

Scenario 2: You already have a relevant page covering the topic.

If you already have a page, compare it with both the top-ranking Google pages and the most frequently cited AI sources.

Look for gaps in content coverage, supporting evidence, product examples, and clarity.

When optimizing for both channels, keep these considerations in mind:

• For AI visibility, pay particular attention to whether the page gives direct answers and whether individual sections can be understood without needing several paragraphs of context.
• For Google, continue applying normal on-page SEO practices, including keyword relevance, internal linking, metadata, and search intent.

The objective is to create one useful page that can compete for relevant Google rankings while also providing information AI search systems can retrieve and reference.

You don't necessarily need separate versions of the same article for traditional SEO and AI SEO.

Step 4. Build topical authority through original research, supporting content, and internal linking

Optimizing an individual page can improve its relevance, but businesses also need to consider how their content covers the broader topics associated with their products.

For example, a CRM software company might publish a primary guide about CRM software for startups.

That guide could link to supporting articles covering:

• CRM implementation for small sales teams.
• CRM integrations and automation.
• CRM pricing and product comparisons.
• Sales pipeline management.
• CRM migration and setup.

Each supporting page should answer a specific question or serve a clear buyer need. The internal links help users move between related subjects and make the website's content easier to discover.

This is relevant to traditional SEO, where internal linking and topic coverage help search engines understand the relationship between pages. It can also support AI search because complex questions may require information from several related sources.

However, publishing a large number of similar pages isn't enough. The content needs to provide distinct, useful information rather than repeating the same explanations across multiple URLs.

If your company has conducted a survey, analyzed product usage data, published customer results, or developed a useful industry benchmark, that information can give other websites a reason to reference your content.

For example, at ScaleTheory, we published original research estimating the size of AI search compared with traditional search engines. The research includes the data, assumptions, and methodology behind the findings, giving other businesses and publications information they can examine and cite.

For B2B SaaS companies, original research doesn't always require a large industry survey. It might involve analyzing anonymized product usage trends, comparing implementation outcomes, or publishing a detailed customer case study with measurable results.

The important part is that the information contributes something useful that readers cannot find in every competing article.

Step 5. Strengthen backlinks, brand mentions, and third-party citations

Once your content strategy is in place, the next step is to improve your authority and brand presence across external websites.

For traditional SEO, relevant backlinks continue to be important. For AI SEO, you also need to consider which third-party websites are appearing as citations for your target buyer queries.

1. Build relevant backlinks for traditional SEO

Identify the pages and keywords where you want to improve rankings and look for appropriate link-building opportunities through industry publications, original research, partnerships, and editorial coverage.

The objective is to earn relevant backlinks that support your website's authority and ranking potential for important commercial topics.

2. Improve brand presence on frequently cited third-party websites

For AI SEO, extend this research to the websites that AI platforms frequently cite for your target prompts.

For example, suppose ChatGPT repeatedly cites a third-party article comparing CRM software for startups. The article includes several competitors but doesn't mention your company.

If your product is relevant to the comparison, that page could be an outreach opportunity. You can contact the publisher, explain why your product deserves consideration, and provide accurate product information or supporting evidence.

Prioritize relevant editorial opportunities rather than trying to manufacture mentions across unrelated websites.

The same approach can apply to:

• Industry publications.
• Software directories and comparison articles.
• Review platforms.
• Relevant community discussions.

You can identify these opportunities by examining the citation sources collected through your AI visibility research. This makes your outreach more focused.

Instead of relying only on traditional link-building metrics, you can consider which websites are already appearing in AI-generated answers for the topics your business wants to own.

The combined approach is straightforward:

• Build relevant backlinks that support traditional search authority and rankings.
• Improve your brand's presence on credible third-party sources that buyers and AI search platforms reference.

Some activities will contribute to both.

For instance, an industry publication covering your original research might link to your website while also describing your company and expertise.

Continue tracking rankings, citations, and brand mentions to understand how your presence changes over time.

How Gloroots doubled inbound leads in 4 months with ScaleTheory

See how ScaleTheory helped Gloroots recover organic growth and improve visibility across Google and LLMs. Organic MQLs doubled within 4 months, and AI search went from zero to 20+ monthly leads.

Read the full case study ->

How to measure traditional SEO and AI SEO performance

When businesses optimize for both traditional search and AI-generated answers, performance measurement needs to cover both.

Traditional SEO already has established metrics for understanding search rankings, website traffic, and conversions. AI SEO introduces additional visibility indicators, particularly for interactions where users receive recommendations without visiting a website.

Review these measurements together rather than treating AI visibility as an isolated reporting activity.

1. Track keyword rankings, organic clicks, and website traffic from traditional search

Continue monitoring traditional SEO performance through metrics such as:

• Keyword rankings for commercially relevant search terms.
• Organic impressions and clicks.
• Click-through rate from search results.
• Organic website traffic.
• Leads and conversions generated through organic search.

Google Search Console can help track search impressions, clicks, and ranking performance. Web analytics platforms can provide information about the traffic and conversions generated from organic search. These metrics remain useful for understanding how your website performs in traditional search listings.

However, a decline in clicks doesn't always mean a page has lost its ranking position. AI Overviews and other search features can change what users see and how they interact with the results.

For this reason, rankings, impressions, and click-through rates should be examined together.

2. Track how often AI search engines mention and cite your brand

AI visibility measurement begins with understanding how frequently your brand appears in responses to relevant buyer prompts.

For example, if you track 50 prompts related to CRM software and your brand appears in 20 responses, your observed mention rate is 40%.

You can calculate it as:

Brand mention rate = Responses mentioning your brand ÷ Total responses tested × 100

Citation tracking measures how frequently AI platforms reference pages from your website as supporting sources.

Website citation rate = Responses citing your website ÷ Total responses tested × 100

Use consistent definitions when calculating these metrics. Also track which specific pages receive citations and the prompts associated with those citations.

A research article may receive frequent citations without the company itself being recommended as a product. This is why brand mentions and website citations should be measured separately.

3. Compare how often AI recommends your brand versus competitors

Your brand's mention rate tells you how frequently it appears in tracked responses. Competitor comparisons help explain whether you're gaining or losing visibility within your category.

Suppose you're tracking 50 prompts related to CRM software for startups. Your results might show:

In this example, your brand appears less frequently than the two competitors in the tested responses.

You can use this information to identify where competitors are being recommended and investigate which sources support those answers. This type of comparison is often described as AI share of voice.

The important part is maintaining a consistent set of prompts and measurement conditions so results are comparable over time.

4. Measure website visits, leads, and conversions from both traditional and AI search

Visibility should eventually be evaluated against business outcomes.

• For traditional SEO, you can track organic search traffic, demo requests, form submissions, and other conversions through web analytics and CRM reporting.
• For AI SEO, monitor identifiable referral traffic from platforms such as ChatGPT and Perplexity where referral information is available. Then examine what those visitors do after reaching your website.

Relevant metrics include:

• AI referral sessions.
• Demo requests and trial signups.
• Qualified leads.
• Opportunities and pipeline associated with identifiable referrals.

You should also consider self-reported attribution.

For example, adding a question such as "How did you hear about us?" to a demo form can help identify buyers who first discovered your company through ChatGPT or another AI platform.

These responses won't capture every AI-influenced interaction, but they can provide information that website referral tracking misses.

Branded searches and direct traffic can provide additional context, although increases in those metrics alone don't prove that AI visibility caused the change.

For B2B SaaS companies, the objective is to understand whether growing visibility is reaching relevant buyers and contributing to qualified pipeline.

5. Monitor changes in Google search performance and AI visibility over time

A one-time audit provides a baseline. To evaluate the impact of your SEO activities, you need to measure performance consistently.

• For traditional SEO, review how rankings, organic traffic, clicks, and conversions change for your target topics.
• For AI SEO, monitor the same set of relevant buyer prompts across the platforms you're tracking. Record how frequently your brand appears, which pages receive citations, and how often competitors are recommended.

AI responses can vary between runs and change with platform updates, so repeated testing is more useful than drawing conclusions from one response.

Review these findings together.

For example, a page might improve its Google ranking without receiving more AI citations. Another page might begin appearing in AI-generated responses while its traditional ranking remains relatively unchanged.

These differences can help determine whether you need to improve content relevance, supporting information, technical accessibility, or third-party brand presence.

The purpose of measurement is to decide which activities deserve more attention and whether your combined SEO strategy is improving visibility for the topics that matter to your business.

Are you tracking your visibility across Google and LLMs?

Traditional SEO rankings tell only part of the story. Track where your brand appears in AI-generated answers, compare competitor recommendations, and identify opportunities to improve search visibility with ScaleTheory.

Improve Your AI Visibility

How ScaleTheory helps brands improve visibility across Google and AI search

Traditional SEO continues to account for most search demand, but AI-generated answers are changing how people discover and evaluate information. Our research suggests that this change is happening within Google itself as much as it is through the growth of LLM platforms.

For B2B SaaS companies, the practical approach is to continue improving traditional search performance while expanding SEO activities to account for AI-generated answers.

At ScaleTheory, we approach AI SEO as an extension of traditional search optimization.

Our strategy focuses on:

• Identifying the commercial topics and buyer queries that matter to a business, across both Google and LLM platforms.
• Analyzing Google search performance alongside AI-generated responses to understand where the brand has visibility, where competitors are appearing, and which opportunities should be prioritized.
• Combining traditional SEO with AI search optimization, including keyword and prompt research, content creation and optimization, technical SEO, topical authority development, and third-party brand visibility.
• Using VisibilityX, our AI visibility tracking tool, to monitor brand mentions, citations, and recommendations across relevant AI platforms while continuing to track traditional search rankings, traffic, and business outcomes.

These findings help us identify opportunities for brands to improve their presence in AI-generated answers while continuing to strengthen traditional organic search performance.

The objective is to improve visibility wherever potential customers are researching solutions and connect that visibility to qualified demand and pipeline.

If you want to understand how your brand performs across Google and AI search, and where you can improve, book a demo with ScaleTheory.

FAQs

1. What is the main difference between AI SEO and traditional SEO?

Traditional SEO focuses on improving website rankings and generating organic traffic from search engines like Google and Bing. AI SEO focuses on improving brand mentions, citations, and recommendations in AI-generated answers across Google AI Overviews, ChatGPT, Perplexity, and other AI platforms.

2. Is AI SEO replacing traditional SEO?

AI SEO is changing how businesses approach search optimization, but it is not replacing traditional SEO. Google still accounts for most search demand, while AI-generated answers are becoming more common within Google itself. Businesses need both approaches to maintain visibility across search results and AI answers.

3. Does ranking higher on Google improve AI search visibility?

Higher Google rankings can help content get discovered by Google's AI search features, but they don't guarantee inclusion in AI-generated answers. AI platforms may also reference other websites, reviews, and third-party sources when generating responses.

4. Do businesses need separate content for traditional SEO and AI SEO?

No. Businesses can optimize the same content for both traditional and AI search. This involves targeting relevant keywords, answering buyer questions directly, using clear headings, including original research, and providing useful supporting information.

5. How can businesses measure the success of AI SEO?

Businesses can measure AI SEO performance by tracking how often their brands are mentioned, cited, and recommended across relevant AI search prompts. They should also monitor competitor visibility, AI referral traffic, leads, and conversions alongside traditional SEO metrics such as rankings and organic traffic.