What is AI SEO?
AI SEO (Artificial Intelligence Search Engine Optimization) is the overall discipline of optimizing a company's digital visibility to be recommended and cited by AI-powered search engines, while maintaining and strengthening classic Google visibility.
Where classic SEO is about ranking high in Google's organic results, AI SEO is about convincing a language model that your content is the most credible and relevant answer to the user's question. It requires a deep understanding of how AI models retrieve, evaluate and synthesize information, and it requires a content and authority strategy designed specifically for the new reality.
AI SEO is not a replacement for classic SEO. It's an extension that ensures your investments also bear fruit in the fastest growing channel.
Why AI SEO has become business critical
The search landscape is undergoing the biggest change since Google's launch. Four trends point in the same direction:
The new reality in numbers (April 2026)
These numbers collectively tell a clear story: More and more customers are making decisions in dialog with AI, often without visiting a website. Those who do visit are more targeted in their research and are further along in the buying process when they arrive. Companies that aren't mentioned in the AI's response not only lose traffic, but they also lose the opportunity to be part of the consideration process altogether.
The industry predicts that AI searches will overtake classic Google search by 2028, giving you a window of a few years to build the authority you need to be visible in the new reality.
AI is changing the messy middle“
Google's concept of “the messy middle” describes the messy phase of the customer journey between the initial impulse and the actual purchase, where the customer researches, compares, evaluates and changes their mind multiple times. Historically, this phase has taken place across Google searches, blog posts, reviews and comparison sites.
AI moves much of this process into conversations with ChatGPT and similar assistants. Customers ask AI for recommendations, compare products, ask for pros and cons and build a short-list before they even visit your website. For you as a business, this means that if you're not part of the AI's answer, you're not part of the decision-making process.
The difference between classic SEO and AI SEO
The two disciplines have the same goal - digital visibility - but they require different tools, signals and metrics. The table below shows where the differences lie:
| Parameter | Classic SEO | AI SEO |
|---|---|---|
| Primary goal | High ranking in organic results | Being cited as a source in AI answers |
| Content format | Long, keyword-dense texts | Accurate, question-based answer blocks |
| Keyword type | Short head terms (“SEO agency”) | Long conversational questions |
| Success KPI | Click-through rate (CTR) and organic traffic | Share of Voice in AI responses |
| Main signals | Backlinks and internal link structure | Schema Markup and E-E-A-T signals |
AI-Search, AEO, GEO and LLMO - key terms explained
Recently, several different terms have emerged, all describing the work to strengthen presence in AI-powered searches. AI SEO is emerging as the overarching umbrella term, but alongside it are a number of more specific terms encountered in practice. Some describe disciplines, others describe approaches, and one describes a technique that you should steer clear of. Here are the most important ones:
The search behavior itself
The umbrella term for all AI-powered search experiences: ChatGPT, Google AI Overviews, Perplexity, Gemini and the like. Where AI SEO is the discipline, AI Search is the phenomenon itself, i.e. the channel being optimized for.
AI-centric SEO strategy
The strategic approach where AI is central both as a goal and as a method: you optimize for AI search engines, while AI tools are integrated into the work itself, from analysis and research to content production and ongoing optimization. The hyphenated spelling emphasizes that AI is at the center of the entire SEO effort.
Answer Engine Optimization
The discipline of structuring content so that it is easily identified and quoted as direct answers by AI models and voice assistants. Focus on precise answer blocks, logical H1-H3 structure and comprehensive Schema Markup.
Generative Engine Optimization
The more specific discipline of optimizing content for generative AI search engines like Google AI Overviews and Perplexity. Closely related to AEO, but with a greater focus on semantic depth and the generative synthesis.
Large Language Model Optimization
Working to optimize content and technical infrastructure to be quoted by major language models like ChatGPT, Gemini and Claude. LLMO embraces both AEO and GEO under one roof.
Mass production of pages (beware)
The technique of automatically generating hundreds or thousands of landing pages from data sets, for example “best [product] in [all cities]”. Can be used legitimately by the biggest data platforms, but is often abused to create thin, generic pages at scale. Google increasingly frames this practice as “scaled content abuse” and AI models rarely cite programmatic pages because they lack semantic depth and credibility signals. A risky shortcut that rarely pays off.
In practice, most of the concepts overlap significantly and the terminology is still evolving. The important thing is not to keep them sharply separated, but to understand that serious AI visibility is always built on the same basic substance: content structured with precision, credible signals and real authority in the market. Shortcuts like programmatic SEO promise quick results but undermine the very signals needed to be selected by both Google and the AI models.
How AI models make their choices
When a user asks a question to ChatGPT or Google AI Overviews, three things happen in a matter of seconds:
The question is split up
The AI breaks the question down into several sub-questions so it can cover all the nuances. One question turns into 5-10 internal searches.
The sources are assessed
The AI finds the most credible and relevant sources based on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals.
Put together the answer
The selected sources are synthesized into a single answer, often with citations. Only the companies cited here are given visibility.
The fan-out process has an important consequence: the AI must be able to recognize and understand your content across many different formulations of the same question. This is why semantic depth and question-based content structures are so important. You need to be able to match the question, no matter how it's asked.
Bias, heuristics and how AI makes recommendations
AI models are trained on human text and therefore inherit human decision-making patterns. Three of these patterns directly influence who gets recommended:
- Authority bias: AI values sources with named experts, official organizations, and well-documented credentials over anonymous posts.
- Social proof: Companies that are positively mentioned across many independent sources (media, forums, reviews, LinkedIn) are perceived as more trustworthy.
- Category heuristics: When AI is asked to name the “best X”, it often picks companies that are clearly associated with the category across the web, not necessarily the objectively best.
This means that AI SEO is not just about content on your own website. External publicity, digital PR, strong citations and industry authority play a crucial role. A company that only optimizes onsite but doesn't build external authority will have a hard time breaking through.
The three pillars of AI SEO work
AI SEO work can be divided into three pillars, all of which must be in place for the effort to succeed. None of them work alone, and it's the interaction between them that determines whether you get picked or overlooked by the AI models.
Query portfolio
Mapping out the queries you need to be visible on. A strong query portfolio focuses on the queries your customers are actually asking AI (not the keywords you used to rank for in Google), prioritizes them by commercial value and tracks them continuously. Without a defined portfolio, you're optimizing against everything and nothing at the same time.
Onsite: Optimization and enrichment
Working on your own website: restructuring existing pages into question-based answer blocks, implementing Schema Markup, building E-E-A-T signals (named authors, external citations, technical language) and adding semantic depth. In many cases, it's about refining the content you already have to be citable, not creating something completely new.
Offsite: Digital PR and brand mentions
Everything that happens outside your own site: mentions in industry and niche media, quotes in press coverage, expert posts on authoritative platforms, LinkedIn posts from named experts and partnerships. This is what reinforces AI models' perception of you as a credible player in the category and connects your brand with the intentions customers have when they ask.
A company that only works with one of the pillars rarely gets the full benefit. Onsite without offsite lacks the authority signals needed to be chosen. Offsite without onsite has no gateway to direct traffic to. And both without a sharp query portfolio end up optimizing in directions that don't create commercial value.
How is AI SEO measured? Share of Voice as a new KPI
In classic SEO, success is measured in ranking and click-through rate. In AI SEO, the most important KPI is Share of Voice (SoV), which expresses the proportion of relevant AI responses your brand is mentioned in.
For example, if 100 potential customers ask ChatGPT for recommendations for a supplier area and your brand is mentioned in 35 of the responses, you have a Share of Voice of 35%. That number is measurable, can be tracked over time, and correlates strongly with the commercial value of the AI channel.
Alongside Share of Voice, you also track referral traffic from AI platforms, the quality of leads coming through the AI channel and the development of your classic organic performance. Together, these metrics provide a complete picture of the impact.
Can AI SEO be quantified commercially?
Yes, it is. Share of Voice is a strong starting point, but by itself it doesn't tell you where the commercial value lies. It only does that when coupled with existing data from Google Ads, Google Analytics and Google Search Console.
By looking at which search terms are bid on in Ads, which ones generate impressions and clicks in Search Console, and which ones convert best in Analytics, we get a picture of where the real business value lies. When these data sources are combined with the measurement of your AI visibility, it becomes clear which terms and areas to prioritize to create the greatest impact on revenue and leads.
Where is there purchasing power?
Shows which search terms your competitors are bidding on and where the CPC is high. A high CPC is a strong indicator that the term has commercial value, even if it is not yet driving organic traffic to you.
Where do you stand today?
Shows which organic searches are already driving impressions and clicks to you and where you are ranked. Provides a basis for identifying where there is most to gain, or lose, from the rise of AI search engines.
What really converts?
Shows which pages, traffic sources and campaigns actually deliver leads and revenue. Uncover which areas of your business have the highest value per visitor and where AI visibility pays off best.
The result is that AI SEO is not an abstract visibility project, but a measurable effort tied directly to business KPIs. You know what you get for your investment, and you can continuously adjust according to what really works commercially.
Brand searches: The hidden benefit of increased AI visibility
Alongside Share of Voice and the link to Ads, Search Console and Analytics, there is a more subtle but highly measurable effect of AI SEO work: Increasing brand searches. This is one of the clearest indicators that AI visibility is making a real difference in the minds of customers.
The mechanics are simple. In complex customer journeys, especially in B2B, customers spend days, weeks or months researching before making a decision. When they ask ChatGPT, Perplexity or Google AI Overviews for recommendations in your category along the way, and your brand is mentioned repeatedly, you end up on their mental “net list” - the short list of suppliers that are actually considered.
The mechanism here is often referred to as intent-based branding. Classic brand work requires a high frequency of exposure over a long period of time to shift perceptions. AI exposure, on the other hand, hits precisely at the moment when the customer is in active research mode and most receptive to new names. The more often you are mentioned in AI responses to relevant queries, the more likely you are to end up on the customer's shortlist, not as abstract brand awareness, but as a real, actively considered supplier.
From there, predictable behavior follows. The customer searches your brand directly on Google, not the generic category but your company name, to visit the website, read cases and make the next decision. That behavior is directly measurable:
- Google Search Console Shows increase in impressions and clicks on branded queries (your company name and variations thereof)
- Google Analytics shows increase in direct traffic and sessions from users arriving via brand searches
This means that the effect of AI SEO work doesn't only show up in Share of Voice. It also works indirectly through classic search data, such as the increased brand awareness that comes from being the recommended option in the AI conversation. Brand searches typically have significantly higher conversion rates than generic searches because the customer has already formed an opinion about you before they click.
How to get started with AI SEO
A serious AI SEO effort follows a structured sequence. The order matters because each step builds on the previous one:
Baseline analysis
Map your current Share of Voice. You can't improve what you don't measure.
Technical AI readiness
Ensure AI crawlers can find, read and cite your content. Schema markup and crawlability are the foundation.
Content optimization
Optimize and enrich existing content into question-based answer blocks that contain the queries customers typically ask in their buying journey.
Authority building
Build external publicity, digital PR and citable resources that strengthen your position in AI training data and real-time lookups.
Continuous measurement
Track Share of Voice, referral traffic and lead quality. The AI landscape is changing rapidly, so efforts need to be adjusted continuously.
Do it yourself or work with an AI SEO agency?
Some companies choose to build AI SEO expertise in-house. This is a perfectly legitimate approach, especially if you have a large marketing team with the resources to keep up with a constantly evolving field. But there are four reasons why many companies choose to partner with a specialized AI SEO agency instead:
The pace of the field
AI search changes every month. Models are updated, algorithms are fine-tuned and best practices shift. A specialized agency works daily in the field and translates the new insights to you, where an in-house team will inevitably fall behind.
Tools and data basis
Serious Share of Voice measurement requires either expensive SaaS licenses or proprietary analytics platforms, neither of which is profitable for a single company. An agency spreads the cost across many customers and gives you access to data you wouldn't otherwise have.
Experience across industries
An agency sees which strategies work across markets, languages and industries. That experience translates to your context and saves you from many of the mistakes you would otherwise have to make on your own.
Accountability and quality control
Without experience, it's easy to fall into the traps: AI-generated content without professional validation, overlooked technical signals or pure spam tactics that get hit by Google sanctions. An agency vouches for quality and ensures that what is published in your name can carry the day.
The most effective model for most companies is a hybrid partnership: the agency provides strategy, Share of Voice measurement and the heavy lifting, while your in-house team takes over more of the day-to-day operations. This gives you expertise without creating dependency.
Want to know more about an AI SEO collaboration?
Read more about how we as an AI SEO agency help companies get recommended in ChatGPT, Google AI Overviews and Perplexity, or contact us directly for a non-binding dialog.
