What is the algorithm of Google AIO?

Google’s AI Overview (AIO) algorithm represents a fundamental shift in how search works, moving from traditional ranking systems to a generative, reasoning-driven approach that creates comprehensive answers by processing queries through large language models, neural networks, and dense retrieval systems. Rather than simply matching keywords and ranking pages, the algorithm generates dozens of hidden sub-queries, evaluates content at the passage level, and builds logical reasoning chains to construct responses that aim to answer questions directly within the search interface.

If you’re an online marketer trying to understand this new landscape, you’re facing a paradigm shift that goes well beyond traditional SEO. The rise of AI-powered search features means rethinking how we create, structure, and optimise content for visibility. Let’s explore exactly how this algorithm works and what it means for your digital marketing strategy.

How Google AIO algorithm works

At its core, Google’s AI Overview system operates on a stateful conversational model that maintains a persistent memory of each user. This happens through what’s called user embeddings, essentially a dense vector representation of your behaviour across the entire Google ecosystem. Your search history, location data, clicks, and even Gmail content all contribute to this profile.

When you enter a search query, something fascinating happens behind the scenes. The system initiates what’s known as a “query fan-out”, where the AI generates dozens or even hundreds of synthetic sub-queries in parallel. These aren’t just keyword variations; they’re a diverse set of questions designed to explore every facet of your potential intent:

• Implicit queries (what you likely meant but didn’t say)
• Comparative queries (pitting products or concepts against each other)
• Entity-expanded queries (swapping general terms for specific brands)
• Personalised queries (factoring in your location and preferences)

The technology relies heavily on dense retrieval and vector embeddings. Unlike traditional search that matches keywords lexically, AI Overviews determine relevance through mathematical similarity between embeddings of queries, passages, and user profiles. This is why content that seems to rank poorly for your original query might appear prominently in an AI Overview, as it likely ranks highly for one of those hidden synthetic queries.

The system evaluates content at the passage level, not the page level. A single well-crafted, semantically complete sentence can be chosen to inform the response, even if the rest of the page is irrelevant. This granular approach allows the AI to build logical reasoning chains, selecting content not just for its authority but for its ability to support specific logical steps in constructing a comprehensive answer.

Key ranking factors for AIO visibility

The factors that influence AI Overview visibility differ significantly from traditional SEO signals. Based on recent analysis, brand web mentions demonstrate the strongest correlation with AI Overview visibility, even more so than backlinks or domain authority. This shift reflects how Large Language Models train on extensive web text, making text-based signals particularly influential.

Here are the most impactful ranking factors for AI Overviews:

Content depth and topical authority remain crucial, but they’re evaluated differently. The AI looks for content that can answer not just the stated query but also the constellation of related questions it generates. This means comprehensive coverage of a topic becomes even more important than targeting specific keywords.

User engagement metrics play a role, but in a more nuanced way. Since the goal is often to provide answers without clicks (zero-click behaviour), success isn’t measured by traditional clickthrough rates. Instead, the algorithm considers whether content effectively supports the AI’s reasoning process and provides value within the generated response.

What makes content eligible for AI Overviews?

Not all content has equal chances of appearing in AI Overviews. The system shows clear preferences for certain formats and characteristics. Content with short, direct definitions or summaries near the top of the page performs particularly well, as does content formatted with step-by-step instructions or numbered lists.

Query types that commonly trigger AI Overviews include:

• How-to questions requiring procedural answers
• Definitional queries seeking clear explanations
• Comparison queries evaluating multiple options
• Factual questions with objective answers
• Product-related queries where recommendations add value

The most successful content for AI Overviews shares several characteristics. It’s structured with clear labels and subheadings, making it easy for AI to extract specific information. The writing is concise and direct, avoiding walls of text that are difficult to parse. Most importantly, it provides comprehensive coverage of topics, anticipating and answering follow-up questions users might have.

Content doesn’t need to rank in the number one position to appear in AI Overviews. If your content is structured clearly and provides unique value for specific aspects of a query, it can be selected even from lower-ranking positions. This opens opportunities for sites that might struggle with traditional SEO but excel at creating well-structured, informative content.

Common challenges with AIO optimization

The shift to AI-driven search presents several significant challenges for marketers. Perhaps the biggest obstacle is the lack of transparency in how the algorithm actually works. Unlike traditional SEO where we can track rankings and understand specific signals, AI Overviews operate more like a black box, making it difficult to reverse-engineer what works.

Tracking performance poses another major challenge. Google Search Console doesn’t indicate whether your content appears in AI Overviews, and traditional rank tracking tools are essentially obsolete in this context. Since two users can get wildly different AI-generated answers for the same query due to personalisation, logged-out rank tracking becomes meaningless.

Creating content that satisfies both traditional SEO and AI requirements often feels like serving two masters. While SEO might reward keyword optimisation and link building, AI in digital marketing favours clarity, structure, and comprehensive coverage. This dual requirement can lead to conflicting optimisation strategies.

The zero-click phenomenon creates a fundamental challenge for business models built on website traffic. When AI provides complete answers directly in search results, users have less reason to visit your site. This shift requires rethinking success metrics and finding new ways to derive value from search visibility.

Measuring AIO algorithm performance

Measuring success in the AI Overview era requires new approaches and metrics. Traditional tools fall short because they’re built on outdated models of lexical analysis and static rank tracking. Instead, marketers need to track “Share of Answers” across multiple AI surfaces, including not just Google but also ChatGPT, Perplexity, and other AI platforms.

Effective measurement strategies include:

• Manual testing of top keywords across AI tools to see if your content is mentioned
• Tracking citation frequency rather than rankings
• Monitoring brand mentions within AI responses
• Using specialised tools that can detect AI platform traffic
• Running multiple queries for the same topic to account for variation

Attribution becomes particularly challenging when your content contributes to an AI response but doesn’t generate a click. Some automated SEO tools using AI are beginning to address this gap, but the technology is still evolving. The focus shifts from tracking direct traffic to understanding how often your content influences AI-generated answers.

Connecting AIO appearances to business outcomes requires creative thinking. Rather than relying solely on traffic metrics, consider tracking brand awareness, citation quality, and the competitive advantage of being the source AI systems trust for your topic area. These softer metrics might better reflect the true value of AI Overview visibility.

Future evolution of Google’s AI algorithm

The trajectory of Google’s AI algorithm points toward an increasingly sophisticated system that will fundamentally reshape search. Based on current developments, we can expect AI Overviews to become more conversational, handling complex multi-turn queries that build on previous questions. This evolution aligns with the broader trend of AI content becoming more nuanced and context-aware.

Future developments will likely include:

• Enhanced multimodal capabilities, seamlessly blending text, video, and image responses
• Deeper personalisation based on user context and real-time signals
• More sophisticated reasoning chains that can handle complex, ambiguous queries
• Integration with other AI systems for specialised domain knowledge
• Improved citation and attribution mechanisms

For long-term SEO strategy, this evolution suggests a shift from traditional optimisation to what’s being called “relevance engineering”. This new discipline focuses on structuring content for machine reasoning, understanding performance in vector space, and creating content that wins head-to-head comparisons at the passage level.

The rise of generative engine optimization represents more than just a new set of tactics; it’s a fundamental rethinking of how we create and structure content for discoverability. As AI systems become the primary interface between users and information, success will depend on understanding and adapting to these new paradigms rather than clinging to traditional SEO practices.

Understanding Google’s AI Overview algorithm isn’t just about keeping up with the latest search features, it’s about preparing for a future where AI mediates most information discovery. By focusing on clear structure, comprehensive coverage, and passage-level quality, you can position your content to thrive in this new landscape. The marketers who succeed will be those who embrace this shift, moving beyond keyword rankings to become trusted sources that AI systems consistently reference and recommend.