Google SGE & AI Overviews: The 2026 SEO & GEO Playbook

With the advent of the latest innovations in generative AI, Google is preparing to revolutionize the world of online search. At the heart of this revolution is Search Generative Experience (Google SGE), a new feature designed to offer users more comprehensive and contextually rich answers thanks to the advanced use of Artificial Intelligence.

Google’s promise is clear: to radically change users’ web search experience by providing them not just a simple list of useful results, but increasingly detailed and personalized answers based on an increasingly sophisticated ability to interpret queries.

This innovation, currently in the experimental phase, is not yet accessible in Italy or other European Union countries, but it is already the focus of numerous debates and a source of concern. For our part, for now, we can only limit ourselves to observing what is happening overseas to hypothesize the impact Google SGE will have on search engine results rankings (SERPs) and the digital advertising industry.

But what exactly is Google’s Search Generative Experience, and how does it work? In this article, we will try to uncover all the details of this innovation, exploring its potential and implications for the future of online search.

The Technological Foundation: From SGE to AI Mode

To optimize for the new search engine, one must first understand the machine that powers it. The terminology has shifted from the broad “SGE” label used during beta testing to specific product names that denote different user experiences and underlying technologies.

1. The Architecture of Synthesis: Gemini, PaLM 2, and Beyond

The core of Google’s new search capability is not a single algorithm but a stack of large language models (LLMs) and multimodal systems. While early iterations of SGE relied on PaLM 2 (Pathways Language Model), the system has largely migrated to the Gemini architecture.

Gemini’s Multimodal Native Advantage:

Unlike previous models that were trained on text and then “taught” to process images through separate vision encoders, Gemini was trained natively on multimodal datasets. This means the model understands text, code, audio, image, and video simultaneously.

• Gemini Flash: Used for standard AI Overviews where speed (latency) is critical. It provides quick summaries and identifies relevant links.
• Gemini Pro/Ultra: Deployed for “AI Mode” and “Deep Search” queries requiring complex reasoning, multi-step planning, or creative synthesis.

This architectural shift allows the search engine to perform tasks that were previously impossible. For instance, a user can upload a video of a broken appliance, and Gemini can identify the model, diagnose the issue from the audio, and generate a repair guide by synthesizing data from manuals and YouTube videos. This capability moves search beyond text matching into concept matching.

2. Query Fan-Out and Deep Reasoning: How the Engine Thinks

The defining characteristic of the new search stack is its ability to break down complex intent. In traditional search, a complex query often resulted in a “no results” page or irrelevant partial matches. In the AI era, Google utilizes a technique known as Query Fan-Out.

The Fan-Out Mechanism:

When a user asks a nuanced question—for example, “Compare the tax implications of an LLC vs. S-Corp for a freelance graphic designer in California making $100k”—the system does not attempt to find a single page matching that entire string. Instead, the Gemini model deconstructs the prompt into component sub-queries:

1. Sub-query A: “LLC tax benefits California”
2. Sub-query B: “S-Corp tax benefits California”
3. Sub-query C: “Freelance graphic designer tax deductions”
4. Sub-query D: “California franchise tax board LLC fees 2025”

The search engine executes these searches in parallel, retrieves the top-ranking documents for each, and “reads” them. The LLM then synthesizes the findings into a cohesive narrative, resolving conflicts (e.g., ensuring federal tax advice doesn’t contradict California specific rules) through a “Deep Reasoning” layer.

Strategic Implication: This mechanism renders the concept of “long-tail keyword optimization” obsolete in its traditional form. One cannot optimize for the unique, complex user query because it likely has zero search volume. Instead, one must optimize for the component sub-queries. By becoming the authority on “California LLC fees,” a site increases its probability of being cited in the synthesized answer for any complex query involving that topic.

3. The Multimodal Stack: Integrating Video, Image, and Text

The “S” in SGE stood for “Search,” but the experience is increasingly visual. The Gemini architecture allows Google to index the contents of video and audio files without relying on transcripts provided by creators.

• Video Indexing: The model can identify specific segments within a YouTube video that answer a user’s question and present that 15-second clip as a primary source in the AI Overview.
• Visual Search: Users can circle items in images (Circle to Search) or upload photos to prompt the AI. The Shopping Graph then matches these visual inputs against 50 billion product listings to find purchasable matches.

This multimodal capability demands that SEO strategies expand beyond text. Images must have descriptive, entity-rich file names and alt text. Video content must be structured with chapters and clear audio explanations to ensure the “visual” fan-out mechanism can retrieve and cite the media.

Anatomy of the Post-SGE SERP: Generative UI and Interaction

The visual landscape of the Search Engine Results Page (SERP) has evolved from a static list of links to a dynamic, modular interface known as Generative UI. This shift fundamentally alters how users interact with information and how websites earn visibility.

1. Visual Deconstruction of the AI Overview (AIO)

The AI Overview is not merely a text box; it is a complex container of interactive elements. Understanding its anatomy is crucial for “Position Zero” optimization.

Key Components:

• The Snapshot: A text summary (2-4 paragraphs) that directly answers the query. This text is generated by the LLM but grounded in retrieved documents.
• Link Cards (The Carousel): Situated either to the right of the text (desktop) or below it (mobile), these cards feature the source’s favicon, page title, and a thumbnail image. These are the primary traffic drivers. Clicking a card often highlights the specific sentence in the Snapshot that the link supports.
• Expansion Toggles: Many AIOs appear in a collapsed state (“Show more”), requiring user interaction to view the full answer. Optimization efforts must focus on answering the query so effectively that the source is cited in the visible portion of the summary.
• Follow-up Chips: Buttons at the bottom of the AIO (e.g., “Ask a follow up,” “Simplest explanation”) encourage conversational depth, keeping the user within the Google ecosystem rather than clicking out to a website.

2. Generative UI: Dynamic Component Rendering and A2UI

In a significant leap forward, Google’s 2025 updates introduced “Generative UI” capabilities. This allows the AI to not just write text, but to write code that renders custom user interfaces on the fly.

Mechanism: Using the A2UI (Agent to UI) framework, the AI agent sends a description of a component tree to the browser. The browser then renders native widgets without the need for pre-built templates.

• Example: If a user searches for “compare 30-year mortgage rates for 700 credit score,” the AI does not just list rates. It generates an interactive table with sliders for down payment and interest rate adjustments directly in the SERP.
• Interactive Elements:
  • • Hovercards: Popovers triggered by the [interestfor] attribute allow users to hover over technical terms in the AI summary to see definitions, reducing the need to click away for clarification.
    • Native Carousels: Using CSS scroll-markers, the AI creates swipeable lists of products or images.
    • Custom Select Menus: The AI can build dropdown menus that allow users to filter the AI’s own response (e.g., “Filter by Price” or “Sort by Rating”).

Implication: This pushes “Zero-Click” to “Zero-UI-Development.” Google is effectively building a custom app for every query, further reducing the incentive for users to visit external tools or calculators.

3. The Evolution of “Position Zero” and Link Cards

The “AI Overview” occupies the prime real estate previously held by Featured Snippets, but with greater dominance.

• Displacement: The AIO block often pushes the first organic “blue link” below the fold, sometimes to pixel depth 1200+ on desktop. This means the traditional #1 organic ranking is effectively invisible on initial load.
• The Citation Economy: Visibility is now binary: either you are cited in the AIO (Link Card), or you are invisible. Studies show that the overlap between sites ranking in the top 10 organic results and sites cited in the AIO is dropping (below 20% in some verticals). This suggests that AIO algorithms prioritize different signals (informational density, semantic match) than the core ranking algorithm (backlinks, age).

The Economic Reality: CTR Collapse and the Zero-Click Crisis

The deployment of AI Overviews has triggered a redistribution of the digital attention economy. The “Zero-Click” phenomenon—where a user’s need is satisfied without visiting a website—has accelerated from a trend to a dominant reality.

1. Quantifying the Decline: The 60% CTR Drop

Data aggregated from major SEO platforms and agency studies in late 2025 provides a stark quantification of the impact.

Organic CTR Impact (September 2025 Data) :

Metric	Non-AIO Query	AIO Query	Change
Organic CTR	1.76%	0.61%	-61%
Paid CTR	13.04%	6.34%	-51%
Zero-Click Rate	~40%	~69%	+29%

Interpretation:

For queries where an AI Overview is present, organic traffic is effectively decimated for non-cited results. The user behavior is clear: they read the summary. If they need verification, they click a Link Card (citation). They rarely scroll down to the traditional results.

The “Citation Premium”: Crucially, the Seer Interactive study reveals a survival path. Brands that are cited within the AI Overview earn 35% more organic clicks and 91% more paid clicks than those that appear in the standard results below. The AI citation acts as a powerful “social proof” or endorsement, signaling to the user that this specific source is the authority.

2. The Paid Search Dilemma: ROAS Compression and Ad Density

The disruption extends to paid media (PPC). Advertisers are facing a “double squeeze.”

1. Inventory Scarcity: The AIO block takes up massive screen real estate. Ads are often pushed to the very top (above AIO) or sandwiched below it. This reduces the “viewable” inventory for ads, increasing competition for the few prime spots.
2. Lower CTR: Even when ads are visible, the helpfulness of the AI Overview often answers the user’s question before they consider clicking an ad. This has driven Paid CTR down by 68% for AIO queries.

Result: Cost Per Click (CPC) is rising as advertisers bid aggressively for visibility, while Return on Ad Spend (ROAS) is compressing due to lower conversion volumes from top-of-funnel queries. Advertisers are shifting budgets toward “Bottom of Funnel” (shopping) queries where AIOs are less informational and more transactional.

3. Vertical Analysis: E-commerce, Publishing, and SaaS

The impact of AI Overviews is not uniform; it varies significantly by industry and intent.

Publishing & News :

• Impact: Severe. Traffic to “utility” content (weather, stock quotes, simple definitions, “how-to” guides) has plummeted by 20-40%.
• Reason: These queries have “Know Simple” intent. The AI summarizes the answer perfectly (e.g., “What time is the Super Bowl?”). There is no value for the user in clicking a link.
• Outlook: Publishers must pivot to deep investigative journalism, opinion, and personality-driven content that AI cannot replicate.

E-commerce :

• Impact: Mixed. Traffic to generic category pages is down. However, the quality of traffic is up for products integrated into the Shopping Graph.
• Reason: Users use AI Mode to research (“best running shoes for flat feet”). When they finally click a product card, they are highly qualified and ready to buy.
• Strategy: Optimization must focus on Merchant Center feeds and structured data (Price, Stock, Reviews) to ensure products appear in the “Visual Fan-Out.”

SaaS & B2B :

• Impact: Moderate. Top-of-funnel “What is CRM?” traffic is gone.
• Opportunity: B2B buyers use AI to compare software (“Compare Salesforce vs. HubSpot for small business”). Being cited in these comparison tables is critical. B2B marketers must ensure their feature lists and pricing pages are easily parsable by bots to win these comparisons.

Generative Engine Optimization (GEO): The New Standard

As the algorithms evolve, so must the optimization playbook. Generative Engine Optimization (GEO) is defined as the process of creating and structuring content to maximize the likelihood of being synthesized and cited by a Large Language Model.

1. Content Engineering: The “Answer Nugget” Methodology

Traditional SEO focused on keyword density and article length. GEO focuses on information density and structure. The core unit of GEO is the “Answer Nugget”.

The “Answer Nugget” Concept:

An answer nugget is a concise, standalone block of text (40-80 words) that directly answers a specific user question. It is the “atom” that the Query Fan-Out mechanism looks for to construct its response.

Implementation Strategy:

• Question-Based Headers: Use H2s and H3s that mirror the natural language questions users ask (e.g., use “How does solar panel efficiency change in winter?” instead of “Winter Efficiency”).
• The BLUF (Bottom Line Up Front) Format: Immediately following the header, provide the direct answer in plain text. Do not bury the lead. Follow the answer with “The Reason,” “The Evidence,” and “The Nuance.”
• Semantic Closeness: The vocabulary used in the answer nugget should semantically align with the “centroid” of the topic. Using vector embedding tools, SEOs can identify the terms that are mathematically closest to the core topic and ensure they are present.

2. Technical GEO: Entity Graphs and Schema Architecture

While LLMs are text processors, the retrieval systems that feed them rely heavily on structured data. In 2026, Schema Markup is the primary language for communicating Entity Identity to Google.

The Role of Knowledge Graphs:

Google’s Gemini creates a mental model of the world based on “Entities” (People, Places, Things, Concepts) and the relationships between them. Schema markup helps map your content to these entities.

Critical Schema Types for GEO :

• FAQPage: Essential for feeding the Q&A fan-out mechanism. Note: Conversational phrasing in the name property helps match voice queries.
• Organization: Must be robust. Use the sameAs property to link to all verified corporate profiles (LinkedIn, Crunchbase, Wikipedia). This builds “Entity Authority.”
• Person (Author): Critical for E-E-A-T. Link authors to their external credentials and alumni pages.
• Product: For e-commerce, this must include merchantReturnPolicy, shippingDetails, and real-time offers to be eligible for the Shopping Graph.

The JSON-LD Debate: Despite some debate about whether LLMs “read” code, the consensus is that structured data is parsed before the content hits the LLM. It categorizes the content so the LLM knows what it is summarizing. Therefore, comprehensive JSON-LD is non-negotiable.

3. Trust Signals: E-E-A-T, Authorship, and Citation Probability

Generative AI has a “hallucination” problem. To mitigate this, Google’s algorithms are tuned to be risk-averse. They prioritize sources with high E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness).

Factors Influencing Citation Probability:

1. Consensus: The AI looks for facts that are corroborated across multiple authoritative sources. Being a “contrarian” without overwhelming evidence reduces citation chances.
2. Citation Precision: Content that includes data tables, statistics, and direct quotes is easier for the AI to cite accurately. Vague content is harder to reference.
3. Brand Authority: Mentions of your brand on other authoritative sites (Digital PR) teach the Knowledge Graph that you are a trusted entity in your niche.
4. Freshness: For news and commerce, the timestamp of the data matters. The AI prioritizes the most recent verified information.

The Agentic Web: Shopping, Universal Commerce, and Action

The most disruptive trend of 2026 is the shift from “Informational” search to “Agentic” commerce. Google is moving beyond helping users find products to helping users buy them directly through the search interface.

1. The Shopping Graph: The Database of Record

The backbone of Google’s commercial AI is the Shopping Graph. This dynamic dataset contains over 50 billion product listings and is refreshed 2 billion times per hour.

Why It Matters:

When a user asks AI Mode, “Find me a waterproof hiking boot under $150 that is good for wide feet,” Gemini does not guess. It queries the Shopping Graph for products that match those semantic attributes (Waterproof, <$150, Wide Fit).

• Inclusion is Mandatory: If a product is not in the Shopping Graph (via Google Merchant Center), it is invisible to the AI for these queries.
• Attribute Enrichment: SEOs must ensure product feeds are rich with attributes. “Wide feet” might not be in the title, but if the product has a “width” attribute or “review sentiment” indicating good fit for wide feet, the AI will match it.

2. Agentic Checkout and the Universal Commerce Protocol (UCP)

In November 2025, Google unveiled Agentic Checkout, a feature that fundamentally alters the e-commerce funnel.

The Universal Commerce Protocol (UCP):

UCP is an open standard that allows AI agents to execute transactions across different platforms. It standardizes the exchange of product options, inventory checks, and payment processing.

• How it Works:

1. Discovery: User finds a product in AI Mode.
2. Action: User says “Buy it.”
3. Execution: Google’s agent uses UCP to communicate with the retailer’s cart system, verifies stock, applies the user’s stored payment/shipping info (via Agent Payments Protocol – AP2), and completes the order.
4. Result: The user never visits the retailer’s checkout page.

Strategic Implication: E-commerce brands must adopt UCP standards. The “Checkout Page” is dying; the “Checkout API” is the future. Success means minimizing friction for the agent, not just the human.

3. From Information Retrieval to Task Execution

The agentic shift extends beyond retail. The “Let Google Do It” paradigm is taking over service tasks.

• “Let Google Call”: This feature allows users to click a button to have Google’s Duplex AI call a local business to check stock or book a table. The AI negotiates with the human staff and reports back to the user.
• Agentic Workflows: In 2026, search is becoming a “teammate.” Users can ask Google to “Plan a meal for 4, find recipes, and add the ingredients to my Instacart.” This requires deep integration between search, maps, and third-party apps via agentic APIs.

Publisher Strategy: AdSense Compliance and Monetization

For content publishers who rely on display ads (AdSense), the AI era presents a dual threat: the loss of referral traffic reduces ad impressions, and the rise of AI content triggers stricter compliance policies.

1. Navigating AI Content Policies and “Low Value” Penalties

Google has tightened its enforcement of “Low Value Content” (LVC) policies to combat the flood of AI-generated spam.

• The Policy: AI content is allowed in AdSense, provided it adds unique value. However, content that is merely a “re-write” or synthesis of existing facts (which LLMs do by default) is considered “Replicated Content” and has “no commercial value”.
• The Trap: Publishers using raw output from tools like ChatGPT or Grok are seeing mass rejections and account suspensions. The error message is often “Low Value Content.”
• The Fix: To remain compliant, publishers must layer “Human Value” on top of AI drafts. This includes:
  • • Original data or case studies.
    • First-hand experience (“I tested this…”).
    • Unique opinion or analysis that diverges from the consensus.
    • Video or image evidence created by the author.

2. The Monetization Gap: Diversification Beyond Referral Traffic

With 60% of searches ending in zero-click, the “AdSense Arbitrage” model—buying cheap traffic and monetizing it with display ads—is structurally broken.

• Revenue Share Rumors: While there has been significant agitation from publishers for a “revenue share” model where Google pays for using content in AI Overviews, no such program exists for the general web as of early 2026. Only select partners (like Reddit) have licensing deals.
• Diversification Strategy: Publishers must treat Google as a brand-building channel, not a traffic channel.
  • • Newsletter Conversion: The primary goal of any Google visitor must be email capture.
    • Affiliate Integration: Shifting from display revenue (CPM) to affiliate revenue (CPA) aligns better with high-intent “research” traffic that still clicks through.

3. Strategic Pivots: Paywalls, Licensing, and Community

Publishers are responding to the “Great Decoupling” by walling off their gardens.

• Paywalls: Premium publishers are blocking AI scrapers (via robots.txt or tougher firewalls) and forcing users to subscribe for access.
• Data Licensing: Large publishers (News Corp, Dotdash Meredith) are signing direct deals to sell their archives to LLM training companies. This is the new “SEO” for the enterprise—optimizing the training data contract.
• Community: Building owned communities (forums, comments sections) creates a defensive moat. Users return for the people, not just the content.

Case Studies in Adaptation: Winners and Losers

The transition to AI Search has created a bifurcated landscape of clear winners and losers.

1. The Reddit Surge: The Valuing of Human Experience

The Winner: Reddit. The Stats: Between March and June 2025, Reddit’s citation rate in AI results surged from 1.3% to 7.15%, a 450% increase. In some verticals, Reddit commands over 20% of all citations. The Strategy: Reddit capitalized on the one thing AI cannot generate: authentic, messy, human experience.

• Google’s Bias: The “Hidden Gems” update and subsequent algo shifts explicitly prioritized forum content to counterbalance the polished, generic AI content flooding the web.
• Lesson: Brands should invest in “Community SEO.” Having your brand discussed positively in Reddit threads is now a more powerful ranking signal than a guest post on a generic blog.

2. Niche Authority: How Specialized Sites Secure Citations

The Winner: The Lawn Tennis Association (LTA). The Strategy: The LTA focused on “Entity Ownership.” They revamped their site structure to provide definitive, structured answers to specific tennis rules, player stats, and court dimensions.

• Execution: They used Schema markup and “Answer Nugget” formatting to make their data machine-readable.
• Result: They saw a massive spike in AI Overview appearances. When a user asks “What is the tie-break rule in Wimbledon?”, the AI cites LTA as the source of truth.
• Lesson: Niche sites can beat giants like Wikipedia if they offer deeper, more structured data on their specific topic.

The Loser: Generic “How-To” Farms.

The Reality: Sites that relied on summarizing basic info (e.g., “How to boil an egg,” “What is the capital of France”) have seen traffic drops of 40-70%. These queries are now fully satisfied by the AI.

The 2026-2030 Roadmap: Preparing for the Invisible Interface

As we look toward 2030, the concept of “Search” will dissolve into “Assistance.”

1. 1. Headless SEO: Optimization will move away from “pages” and toward “data feeds.” Brands will need to provide APIs and structured feeds that AI agents can query directly. Your “website” will just be one of many front-ends for your data.
2. 2. The Three-Tier Optimization Stack: Future strategy will require a three-pronged approach :

• SEO (Legacy): For the remaining 30% of users who prefer classic search.
• GEO (Generative): For visibility in AI Overviews and Summaries.
• LLMO (LLM Optimization): For inclusion in the training data of future models. This involves ensuring your brand exists in datasets like Common Crawl and is associated with high-quality tokens.

3. Agentic Readiness:

Businesses must prepare their infrastructure for agentic transactions. This means adopting protocols like UCP and ensuring that your inventory, booking systems, and customer service are accessible to AI bots, not just human browsers.

Conclusion:

Navigating all the changes brought about by Google’s Search Generative Experience can seem daunting, especially without a clear understanding of the most effective strategies. However, it’s important not to panic. It’s clear that SGE will have a significant impact, but the precise nature of this impact is still being defined. In the meantime, it’s essential to continue focusing on proven SEO practices.

This means continuing to create high-quality content that precisely matches users’ search intent, strengthening your brand to make it authoritative and trustworthy, and maintaining a strong, natural link profile. Key concepts such as trust, relevance, and authority, along with Google’s E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) guidelines, will be even more crucial for a chance to appear in the SGE generative box.

For everything else, only time will tell how this new scenario will evolve.