GOOGLE AI MODE: HOW DOES IT AFFECT SEO? WHAT YOU NEED TO KNOW

Google AI Mode is a search mode where Google uses AI (optimized from Gemini) to create aggregated answers directly on the SERP, with some reference links. This experience is closer to a “question and answer” than a traditional 10-link list: users ask a question and receive a structured answer, with enough context, with citations to dig deeper when needed.

Strategic consequences: SERPs are increasingly becoming destination instead of just “transfer stations”. Therefore, SEO thinking also shifts from "ranking up" to “being cited by AI + keeping an excellent on-site experience”.

1) Overview

Google is redefining the search experience with AI Mode: instead of just listing 10 links, the results page (SERP) appears with an AI-generated answer – an answer snippet created by AI, Compiled from many reliable sources. Users ask specific questions and immediately receive structured answers, with several reference links to dig deeper. In terms of experience, it's closer to the way we talk to a knowledge assistant than scrolling through a disjointed list of results.

For SEO, the biggest implication is that the center of value shifts right up to the SERP. From SERP being a "transfer station" bringing users back to the website, SERP has now become a "destination" - where simple questions can be resolved on the spot. This does not kill SEO, but raises the bar: instead of just “climbing rankings”, we must be chosen by AI as a reference and maintain an outstanding on-site experience so that the remaining clicks are truly quality.

2) How does AI Mode work?

AI Mode uses a customized Gemini model for search. The difference lies in the information approach:

• Query fan-out (query expansion): From the original question, the system automatically breaks it down into a series of sub-queries (definitions, how-tos, risks, comparisons, tools, real cases...). These queries run in parallel to cover multiple perspectives, reducing the risk of single-source bias.

• Reconcile & merge: AI performs cross-check between multiple sources, finds consistent intersections, eliminates noise, then contextualizes a single answer while still revealing a path for verification.

• Display with selective citation: Unlike 10 links, the number of "slots" for citations in AI answer is much less. This makes the “game” strict: only content that is truly strong in terms of expertise, authority, and trustworthiness will make the cut.

The result is a change in user behavior: with basic informational questions, they have less need to leave the SERP; With complex questions/preparing to make a decision, they still selectively click on the source cited by AI to see details, compare, or find deeper evidence.

3) Impact on SEO: real risks & long-term opportunities

3.1. Risks

• CTR naturally declines on informational queries:when the answer appears on the SERP, the incentive to click on the website decreases, especially for definition/FAQ/general questions.

• Fewer “slots” to be cited: you are no longer in the game of “hitting the top 10”, but rather hit a few reference positions Referin AI answer. Competition is fiercer in E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness).

• Content & technical quality barriers increase: thin articles, keyword stuffing, lack of evidence, poor structure, slow speed... very difficult to be chosen by AI.

3.2. Opportunity

• Less traffic but “filtered”: users clicking from AI answer often have clear intention, looking for deep solutions. Therefore time on page, scroll depth, conversion rate tend to be better.

• Topic authority becomes a sustainable "leverage":building topic clusters (hub & cluster) helps AI understand who you are in that topic. Once you have "enrolled" in authority, the chance of being cited increases over time.

• Good technical optimization → increases "availability" for AI: indexability, speed, structured data... helps AI read - understand - quote more accurately.

4) Is SEO dead? — No, SEO evolves

Traditional SEO revolves around keywords, onpage, backlinks to gain position. SEO in the AI ​​Mode era is about winning position in answers and keeping promises on the page. The focus shifts from tactical manipulation to real content value, evidence, experience, machine understandability and conversion generation.

5) 5-layer strategic framework (in-depth playbook)

Floor 1 — Technical SEO “unsatisfactory agreement”

1. Performance & Core Web Vitals (LCP/INP/CLS): reduce JS blocking, split bundles, preload critical, lazy-load images/videos, use CDN, optimize fonts (preconnect, font-display: swap).

2. Crawlability & Indexability: accurate robots.txt, consistent robots meta, clear canonical clear, handle URL parameters, remove soft 404.

3. Clean & updated XML sitemap: only lists URL 200/indexable/canonical; Submit immediately after structural changes, especially after migration or 301 redirects.
   → Refer to detailed instructions: https://tanphatdigital.com/vi/blog/huong-dan-tao-and-gui-so-do-website-xml-len-google-search-console

4. Information architecture: division level (category → subcategory → post), clear breadcrumbs, avoid orphan pages; HTML sitemap (or hub page) for large clusters.

5. Internationalization (if any): standard hreflang, canonical synchronization between translations, avoiding duplicate "traps".

Quick measurement tip: run Lighthouse and PageSpeed ​​Insights on each template (article, category, landing), not just home. Optimizing by template has the biggest impact on traffic.

Level 2 — Topical Authority (building authority by topic)

1. Topic mapping: lists all user questions according to the journey (overview → how-to → tools → comparison → risks → decisions).

2. Hub & Cluster:

   • Hub (pillar): Pillar articles cover the picture, provide a clear conceptual framework, TOC, link to clusters.

   • Cluster (spokes): how-to, checklist, case study, FAQ, A/B comparison... each article solves a specific "job".

3. Internal context link: 2-way link hub ↔ cluster using natural anchor; place link in content area (not just breadcrumb/footer).

4. Update periodically: keep dateModified substantive, add data, images, cases; Delete redundancy, merge duplicates.

   → Suggested Checklist: https://tanphatdigital.com/vi/blog/technical-seo-la-gi-checklist-technical-seo-website

Floor 3 — “Real” E-E-A-T

1. Experience (experience):add screenshots, demo videos, measurements, test log; describe the context, tools, parameters.

2. Expertise (expertise): explain principles, methods, standards; Avoid general advice.

3. Authoritativeness: author page with bio, certificates, projects; transparent organization page; external signals (mentioned/cited).

4. Trustworthiness: content policy, standard citation, cross-verification; Pay attention to https security, privacy policy, clear contact information.

Level 4 — Structured data & "easy to extract" presentation

1. Appropriate Schema: Article/NewsArticle, FAQPage, HowTo, Product, Organization, Person... fill in all important attributes (author, datePublished, dateModified, headline, image…).

2. Machine-friendly layout: H2/H3 logic, TL;DR heading, bullet/steps, comparison table, “Key takeaways” block; Short sentences, clear ideas.

3. Images/diagrams with meaning:placed near the description, concise alt text, rich in context caption; use new format (AVIF/WebP) when appropriate.

4. Section identifier: if the article is long, attach an anchor so that AI and users can "jump" straight to the part they need.

Level 5 - Measure & optimize "SERP as a product"

1. Monitor impression vs click: especially for informational clusters (where CTR can decrease). Stratify by intent to properly assess expectations.

2. Optimize “valuable clicks”:

   • Title & meta match expectations after users read AI answer.

   • First-screen solves the problem immediately (open concise article with 2–4 sentences).

   • Clear table of contents/anchor; FAQ block corresponding to popular questions.

3. Standard measurement setup: GA4 + GSC, attach scroll event, outbound click, TOC click… to understand deep reading behavior.
   → Reference settings: https://tanphatdigital.com/vi/resources/setup-google-analytics-search-console

6) Case of designing a content cluster "loved by AI": Quick index after posting

Hub: “Quick article index 2025 – Overview & Checklist”.
Goal: Collect the entire process: edit → publish → sitemap → submit GSC → check log crawl → fix index errors.

Suggested cluster:

• Cluster A – Platform techniques:

  • “Configure XML sitemap & submit GSC (step-by-step)” – operating instructions, illustrations, common errors; link to hub article.

  • “Minimum Core Web Vitals for bots to visit regularly” – LCP/INP/CLS according to each template; How to reduce blocking time.

• Cluster B – Diagnose & fix:

  • “Google not indexing: P1/P2/P3 triage process” – soft 404, incorrect canonical, redirect chain, unintentional robots noindex, content quality.

  • “After migration, index/traffic drop: map 301 & check canonical” – sequence before/after opening the crawl valve.

• Cluster C – Content & E-E-A-T:

  • “Write articles with experience traces: how to capture, attach measurement data, describe test context”.

  • “FAQ & TL;DR: increase the likelihood of being excerpted in AI answer”.

Internal linking strategy:

• Each cluster links back to the hub with natural anchors (“see overview checklist”).

• Clusters horizontally link when there is a dependency (for example, articles fix the index pointing to the sitemap & CWV).

• The hub has a table of contents “jump” to each cluster, ensuring users and AI reach the correct part the fastest.

7) Checklist for optimizing images for SEO & speed ↗

1. Format: Prioritize AVIF/WebP for illustrations; SVG for vector/icon; PNG when a transparent background is needed; JPG for photos.

2. Size as needed: export the correct display size; use srcset/sizes to let the browser choose the appropriate variant (1×/2×).

3. Compression & lazy-load: enable loading="lazy" and decoding="async"; Moderate lossless/lossy compression to not break details.

4. Reduce layout shift: declare width/height or aspect-ratio to preserve the frame; preallocate space for hero images.

5. CDN & cache: deliver images via CDN, Cache-Control long-term; fingerprint the file name (hash) for cache-busting when updating.

6. Alt text & caption: write alt according to context (purpose of image in paragraph), avoid keyword stuffing; caption helps AI understand the role of the image.

7. Main image position: placed near the paragraph describing the concept/proving the data; Prioritize early display of important images.

8. Measurement: monitor LCP/INP after changes; compare before/after using PSI/Lighthouse according to the template to ensure real improvement.

8) Editorial process to “pass the AI ​​frame” (Content Ops)

1. Brief according to intent & JTBD: describe the user's question, context of use, desired result.

2. Outline “easy to quote”: H2/H3 in the sequence “Questions → Answers → How-to → Examples → Checklist/FAQ”.

3. Documenting experience: take photos/videos, measurement logs, state conditions/contexts.

4. Review E-E-A-T: has author bio, reference source, update date, risk warnings/limitations when needed.

5. Technical QC: check title/meta, internal links, schema, images, for both reading experience and reader.

6. Release & measurement: attach UTM when needed; track impression/click/scroll/conversion; back to optimal after 2–4 weeks.

• AI Mode doesn't kill SEO – it raises the bar and shifts the focus to being cited by AI + keeping an excellent on-site experience.

• 5-tier playbook: Strong Technical SEO → Topical authority → Real E-E-A-T → Structured data & “easy to extract” presentation → Measure & optimize.

• Action now: clean sitemap, speed up CWV, build hub/cluster, add experience footprint, and set up GA4 + GSC to track “valuable click” behavior.