ARTICLE

Good SEO Is Good GEO: What Marketers Need to Do Right Now to Win in AI Search

Good SEO Is Good GEO: Your Action Plan for AI Search Visibility

Strong SEO fundamentals translate directly to AI search success—here's how to execute Google's guidance this quarter without chasing speculative tactics.

What You Need to Know

AI search created noise: new acronyms (GEO, AEO, "LLM SEO"), new surfaces (AI Overviews, AI Mode), and new anxiety ("optimize for AI or disappear"). The result? Teams chase fragmented playbooks and "silver bullet" checklists that ignore how Google actually selects sources for generative answers.

Google's position is clearer—and more reassuring. In June 2026, Google VP Brendon Kraham stated: "Good SEO is good GEO." [1][2]. Google's Search Central documentation explains why: AI-powered answers use the same underlying ranking and quality systems, applying retrieval-augmented generation (RAG) and query fan-out to retrieve and ground responses in web content [4][51]. GEO vs SEO isn't a fork—it's a continuation of the same fundamentals with sharper execution and better measurement.

This guide is for marketing managers, CMOs, and content strategists who understand basic SEO but need: (1) clarity on what matters now, (2) an action framework validated by Google, and (3) operationalization without turning teams into "AI prompt engineers." You'll get five fundamentals, a practical content-quality audit (the "non-commodity test"), a measurement model using Google Search Console Generative AI performance reporting, and a map of each action to Iriscale's platform capabilities.

Examples include: a running-shoe diagnostic video that qualifies as non-commodity content, a fast mobile site that removes AI-crawling friction, and structured-data use cases that help Google disambiguate entities without treating schema as magic.

Step 1 — Cut Through the Acronym Confusion

Most “new” AI-search disciplines are different names for the same job—earning visibility by being the best source and making that source accessible.

What’s changing:
Google expanded AI features rapidly. AI Overviews reach 2.5B+ monthly active users; AI Mode surpassed 1B monthly users [1]. AI Overviews appear across query types, with visibility ranging from single digits to large fractions depending on category and intent. The user’s “first click” may now be a citation link inside an AI response, not the traditional #1 blue link.

What’s not changing:
Google’s language is explicit: AI responses rely on core Search ranking and quality systems, using retrieval and grounding to provide helpful answers [2][4]. Kraham’s “good SEO is good GEO” isn’t a slogan—it’s a systems statement: generative answers don’t bypass quality; they depend on it [1].

Myths to stop spending time on:

“We need a separate GEO strategy.” For Google surfaces, you need SEO executed to a higher standard, aligned with Google’s AI optimization resource [55]. Treat “GEO” as a prioritization lens, not a separate channel.
“Add LLMs.txt or you won’t be cited.” Google stated LLMs.txt does not impact rankings and is ignored for Search today [64][77]. Keep robots.txt and sitemaps healthy instead.
“Schema guarantees AI Overview citations.” Structured data helps machines interpret content, but it’s not a substitute for quality or uniqueness. Google’s guidance: mark up visible, primary content, validate it, and avoid using markup as a crutch [55][68].

What this means in practice:

If your “GEO plan” is rewriting old pages with AI to “sound authoritative,” you’re moving backward. Google’s AI optimization resource emphasizes helpful, people-first content—not syntactic tricks [55].
If your team debates “chunking content for LLMs,” redirect that energy into building one canonical page that fully satisfies a multi-part query [55][4].
If stakeholders want a “GEO tool,” anchor the conversation on workflows: content quality, technical accessibility, structured data hygiene, and measurement—what Google can actually use [55][68][86].

Do next:

Rename “GEO” internally as AI Search Readiness to reduce fragmentation: one roadmap, shared KPIs, shared technical backlog.
Create a “Stop Doing” list: LLMs.txt, speculative directives, mass AI rewrites, schema spam—none are endorsed paths to better AI visibility in Google [64][55].

Step 2 — Execute the 5 Fundamentals (Google-Validated)

Kraham’s point only helps if you know what “good” looks like in 2026. Google’s AI optimization resource and Search Central guidance consistently point to: helpful content, strong technical foundations, clarity, structured data where appropriate, and continuous refresh + monitoring [1][55][68]. Here are the five fundamentals—with examples and next steps.

Fundamental #1: Build Content Worth Citing (Not Just Ranking)

AI Overviews and AI Mode answer questions; citations support the answer. Your job: be a credible, quotable source. Google’s guidance stresses originality, usefulness, and people-first content [55].

Examples:

A footwear brand publishes a running-shoe diagnostic video: an at-home gait test, common pain points, and decision rules (“If heel strikes cause X, consider Y”). Non-commodity because it includes firsthand methodology and interpretation, not just specs.
A B2B company publishes original benchmarks (even small-sample) with clear methodology and caveats—content AI can cite responsibly because it’s anchored in real-world observation.
A clinic writes a treatment guide with practitioner review, visible author credentials, and “when to see a professional” guardrails—high-trust utility rather than generic summaries.

Do next:

Add answer-first lead paragraphs and descriptive headings so a single section can be extracted cleanly into an AI response [55].
Put the best “citation-ready” statement near the top: a definition, a step list, or a rule-of-thumb with context.

Fundamental #2: Be the Best Page for the Whole Query

Google notes that AI systems may expand queries (“fan-out”) to gather supporting information [4][51]. Pages that comprehensively address adjacent sub-questions often perform better in AI surfaces because they satisfy more retrieval needs [55][4].

Examples:

Instead of “Best trail running shoes,” one canonical guide includes: terrain types, cushioning tradeoffs, sizing, injury considerations, and maintenance—so the page supports multiple sub-questions.
A SaaS pricing page includes FAQ about contracts, data security, implementation time, and integrations—common fan-out branches.
A recipe page includes substitutions, storage, and altitude adjustments—common follow-ups AI systems look for.

Do next:

Consolidate overlapping articles into one canonical, deeply useful resource page where it improves completeness (and reduce internal competition).

Fundamental #3: Technical Accessibility Is the Gate

If Google can’t reliably crawl, render, and understand your pages, you won’t be cited. Google continues to emphasize technical soundness and clear architecture [55].

Examples:

A fast mobile site with stable layout, compressed media, and minimal render-blocking scripts keeps users engaged and reduces crawler friction.
Semantic HTML (real headings, lists, tables) helps systems interpret structure without guessing.
Clean internal linking ensures key pages are discoverable and regularly recrawled.

Do next:

Audit JavaScript-heavy templates: ensure core content and links are present in rendered HTML and not gated behind interactions.
Keep key pages lightweight; Google’s guidance warns against bloated experiences and emphasizes usability [55].

Fundamental #4: Use Structured Data for Clarity (Not as a Hack)

Structured data won’t replace quality, but it can reduce ambiguity—especially for entities, products, and organizations. Google recommends JSON-LD, marking up visible content, and validating implementation [68][55].

Structured-data use cases that help:

Product schema for ecommerce pages (price, availability, SKU) aligned with visible content.
Organization schema with sameAs links to official profiles to help entity disambiguation (when appropriate and accurate).
FAQPage schema only when FAQs are visible and useful (not spam).

Do next:

Validate with Google’s tools and fix warnings; “almost correct” markup often yields “almost no benefit” [68].

Fundamental #5: Refresh, Monitor, and Iterate

Google rolled out Search Console Generative AI performance reports (June 2026), signaling that AI visibility is now measurable at scale [86][49]. Treat this as a product loop: publish, measure, improve.

Examples:

If a page earns AI citations but low CTR, improve snippet alignment: tighten the opening answer, add a clearer value promise, ensure the cited section matches the title.
If AI impressions rise but conversions don’t, adjust the landing experience: faster page, clearer next step, better comparison table, stronger trust elements.
If competitor brands are cited for “best option” lists, publish your own data-backed comparison criteria (not fluff) and earn relevance.

Do next:

Set a monthly AI visibility review cadence (more on metrics in Step 4).

Step 3 — Run the “Non-Commodity Test” (Content-Quality Audit for AI Citation)

Most teams don’t have a “content problem.” They have a commodity problem: too many pages that say what everyone else says. Generative systems synthesize common knowledge—so commodity content is the easiest to paraphrase and the least necessary to cite. Google’s AI optimization resource emphasizes unique, helpful, people-first information; AI content isn’t penalized by default, but unhelpful content is [55]—and commodity pages are often unhelpful by definition.

Use this practical non-commodity test to audit priority pages (start with top revenue categories and top “how-to” content).

The Non-Commodity Test (Score Each 0–2)

Firsthand input present? Do you include original photos/videos, measured results, real workflows, or expert review—something that proves experience?
Decision utility? Does the page help a user choose/act (rules, checklists, tradeoffs), or only describe?
Specificity and constraints? Does it address “it depends” scenarios, edge cases, and exclusions?
Evidence and references? Do you cite your own methodology, data, or authoritative sources (not random blogs)?
Extractable clarity? Could one section be quoted as a complete, correct answer?

Examples

Running-shoe diagnostic video (non-commodity win): A page embeds a short video showing a gait test plus a table mapping outcomes (overpronation, neutral, supination) to shoe features and injury considerations. Add a brief “why this works” explanation and a disclaimer. That combination is both useful to humans and quotable for AI.
Commodity loss: “Top 10 running shoes in 2026” that rehashes specs from manufacturer pages without any testing method, comparison criteria, or user-fit logic. AI can synthesize this without citing you.
B2B non-commodity win: A “migration checklist” that includes a timeline template, stakeholder roles, common failure points, and screenshots from real implementations (sanitized). AI can cite a step list because it’s concrete and operational.

Do next:

Pick 10 pages for a two-week sprint. Upgrade them to pass the test by adding: a unique asset (video/table/template), decision logic, and a strong answer-first section.
Publish “small original data” consistently. Even simple internal benchmarks (with methodology) can differentiate you—uniqueness is a compounding advantage in AI citation eligibility [55].

Step 4 — Measure GEO with Google Search Console

If GEO is “earning citations in generative answers,” you need a measurement layer that matches that reality—without abandoning business outcomes. Google’s Generative AI performance reports in Search Console (rolled out June 3, 2026) are the key starting point [86][49]. These reports count appearances of clickable citations in AI Overviews, AI Mode, and AI Discover cards, with data beginning May 18, 2026 and a short reporting delay [86][93].

What to Track (Three Tiers)

Visibility (leading indicators) — AI impressions: how often your pages appear as citations in AI features [86][93]. AI clicks / CTR: whether those citations drive visits.
Engagement (quality signals you control) — Landing-page engagement: scroll depth, time-on-page, key interactions. Page speed and mobile UX: remove friction that turns AI clicks into bounces [55].
Business impact (what leadership cares about) — Leads, trials, purchases, assisted conversions from AI-citation landings. Conversion rate by AI-traffic segment vs traditional organic.

How to Use GSC’s AI Impression Reports (Practical Workflow)

In GSC → Performance, add the Generative AI dimensions (where available) and compare: pages with AI impressions vs pages without; queries that trigger AI citations vs classic results.
Identify citation winners and reinforce them: improve the cited section for clarity and completeness; add internal links to high-converting next steps (pricing, category, demo).
Identify near-misses: pages ranking well organically but getting few AI impressions may lack extractable answers, structured clarity, or non-commodity depth. Apply the Non-Commodity Test (Step 3).

Two Measurement Examples

Ecommerce category page: AI impressions rise for “best stability shoe for knee pain,” but CTR is low. You add a quick decision table near the top, compress images for faster mobile load, and clarify sizing guidance. CTR improves and category PDP clicks increase.
B2B how-to guide: AI clicks grow, but conversions don’t. You add a “download checklist” CTA and a short comparison section linking to product pages. Conversions improve because the AI visitor now has a clear next action.

Do next:

Create a weekly “AI visibility standup”: 30 minutes reviewing AI impressions, AI CTR, top cited pages, and one content/tech fix to ship that week.
Tie AI reporting to revenue by adding UTM conventions (where possible) and segmenting in analytics: “GSC AI citation traffic” as its own channel group.

Step 5 — How Iriscale Supports Execution (Mapped to Google’s GEO Pillars)

Google’s message is consistent: don’t chase hacks—execute excellent SEO fundamentals that translate to AI visibility [1][55]. Iriscale’s value is operational: it turns those fundamentals into repeatable workflows, governance, and reporting across teams.

Below is a direct mapping between Google’s GEO pillars and how Iriscale supports execution.

Pillar A: People-First, Unique, Helpful Content

Google’s guidance: Create high-quality, original, useful content; avoid gimmicks and unnecessary AI-specific tactics [55].

How Iriscale supports it: Content planning workflows that enforce non-commodity requirements (mandatory “firsthand element,” decision logic, and extractable answer blocks before publish). Brief templates that standardize answer-first sections, headings, and “fan-out coverage” so each canonical page satisfies more user needs.

Example execution: Your running-shoe guide brief requires a diagnostic video + decision table + “who this is for” section—so the finished asset is inherently quotable.

Pillar B: Technical Accessibility and Clean Architecture

Google’s guidance: Maintain crawlability, indexability, and strong site experience; AI features still depend on core systems [55][4].

How Iriscale supports it: Site-wide audits and monitoring to keep key templates lightweight, internally linked, and consistently accessible. Issue routing so content, engineering, and SEO don’t lose fixes in backlog limbo.

Example execution: Iriscale flags heavy category templates on mobile and prioritizes a compressed-media and render-path fix to protect both UX and AI-crawl reliability.

Pillar C: Structured Data for Clarity

Google’s guidance: Use JSON-LD, mark up visible primary content, validate implementation, and avoid spam [68].

How Iriscale supports it: Structured-data coverage tracking and validation workflows to ensure markup matches on-page content and is kept current. Entity consistency checks (e.g., Organization + SameAs) to reduce ambiguity where appropriate.

Example execution: Product schema is deployed consistently across PDPs, validated, and monitored for drift when merchandising changes.

Pillar D: Refresh and Relevance

Google’s guidance: Keep content updated and aligned with evolving systems; focus on helpfulness through core updates [55].

How Iriscale supports it: Content decay monitoring and refresh cadences for revenue pages and “AI-citation candidates.” Change logs and governance so updates remain consistent with the original intent and quality bar.

Example execution: Quarterly refresh cycles for “best X” and “how to choose X” guides, with clear evidence updates and revised tables.

Pillar E: Measurement and Adaptation

Google’s guidance: Use Search Console’s generative AI performance reports to track AI visibility [86].

How Iriscale supports it: Unified dashboards that pull GSC AI impressions/clicks into the same reporting layer as traditional SEO and conversions—so AI visibility is measured, not guessed. Workflow triggers: if AI impressions rise but CTR falls, Iriscale prompts an “answer block rewrite” task; if clicks rise but conversions lag, it triggers CRO improvements.

Example execution: A weekly report shows top cited URLs, top AI-triggering queries, and the next best action to increase citation share and downstream conversions.

Checklist: “Good SEO Is Good GEO” Action Template

Stop chasing AI-search hacks (no mass rewrites, no schema spam, no LLMs.txt reliance) [55][64]
Pick 10 priority pages (revenue + top informational) for AI Search Readiness upgrades
Add answer-first paragraphs and descriptive H2/H3 structure for extractability [55]
Pass the Non-Commodity Test: firsthand element, decision utility, constraints, evidence, clarity
Consolidate overlapping content into canonical “best page for the query + fan-out” resources
Ensure mobile performance and technical accessibility (crawl, render, internal links) [55]
Implement/validate structured data (JSON-LD, visible content only, test regularly) [68]
Set a monthly refresh cadence for citation-candidate pages
Turn on and review GSC Generative AI performance: AI impressions, clicks, CTR; segment winners/near-misses [86][93]
Tie AI visibility to business outcomes: landing-page engagement + conversions by AI segment

Ready to Operationalize Google-Approved GEO?

If you want a clean, team-friendly way to turn “good SEO is good GEO” into execution—content upgrades, technical hygiene, structured data governance, and GSC generative AI reporting in one workflow—request an Iriscale demo. Iriscale operationalizes the exact pillars Google recommends for AI search visibility [55][86].

Related Guides

Sources

[1] https://www.digitalapplied.com/blog/google-vp-search-good-seo-is-good-geo-cmo-2026

[2] https://ppc.land/googles-vp-of-search-tells-cmos-good-seo-is-still-all-you-need-for-ai

[4] https://goo.gle/3PO7HfB

[49][86] https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports

[51] https://cloud.google.com/use-cases/retrieval-augmented-generation

[55] https://developers.google.com/search/blog/2026/05/a-new-resource-for-optimizing

[64] https://www.seroundtable.com/google-does-not-endorse-llms-txt-40789.html

[68] https://itxitpro.ae/blogs/structured-data-schema-for-ai-search-how-to-feed-the-machines-in-2026

[77] https://www.facebook.com/SearchEngineJournal/posts/googles-john-mueller-dismisses-llmstxt-as-speculative-for-now-and-says-he-likes-/1472670388236740

[93] https://support.google.com/webmasters/answer/16984139?hl=en

ARTICLE

Good SEO Is Good GEO: What Marketers Need to Do Right Now to Win in AI Search

Good SEO Is Good GEO: Your Action Plan for AI Search Visibility

Strong SEO fundamentals translate directly to AI search success—here's how to execute Google's guidance this quarter without chasing speculative tactics.

What You Need to Know

Step 1 — Cut Through the Acronym Confusion

Most “new” AI-search disciplines are different names for the same job—earning visibility by being the best source and making that source accessible.

Myths to stop spending time on:

“We need a separate GEO strategy.” For Google surfaces, you need SEO executed to a higher standard, aligned with Google’s AI optimization resource [55]. Treat “GEO” as a prioritization lens, not a separate channel.
“Add LLMs.txt or you won’t be cited.” Google stated LLMs.txt does not impact rankings and is ignored for Search today [64][77]. Keep robots.txt and sitemaps healthy instead.
“Schema guarantees AI Overview citations.” Structured data helps machines interpret content, but it’s not a substitute for quality or uniqueness. Google’s guidance: mark up visible, primary content, validate it, and avoid using markup as a crutch [55][68].

What this means in practice:

If your “GEO plan” is rewriting old pages with AI to “sound authoritative,” you’re moving backward. Google’s AI optimization resource emphasizes helpful, people-first content—not syntactic tricks [55].
If your team debates “chunking content for LLMs,” redirect that energy into building one canonical page that fully satisfies a multi-part query [55][4].
If stakeholders want a “GEO tool,” anchor the conversation on workflows: content quality, technical accessibility, structured data hygiene, and measurement—what Google can actually use [55][68][86].

Do next:

Rename “GEO” internally as AI Search Readiness to reduce fragmentation: one roadmap, shared KPIs, shared technical backlog.
Create a “Stop Doing” list: LLMs.txt, speculative directives, mass AI rewrites, schema spam—none are endorsed paths to better AI visibility in Google [64][55].

Step 2 — Execute the 5 Fundamentals (Google-Validated)

Fundamental #1: Build Content Worth Citing (Not Just Ranking)

Examples:

A footwear brand publishes a running-shoe diagnostic video: an at-home gait test, common pain points, and decision rules (“If heel strikes cause X, consider Y”). Non-commodity because it includes firsthand methodology and interpretation, not just specs.
A B2B company publishes original benchmarks (even small-sample) with clear methodology and caveats—content AI can cite responsibly because it’s anchored in real-world observation.
A clinic writes a treatment guide with practitioner review, visible author credentials, and “when to see a professional” guardrails—high-trust utility rather than generic summaries.

Do next:

Add answer-first lead paragraphs and descriptive headings so a single section can be extracted cleanly into an AI response [55].
Put the best “citation-ready” statement near the top: a definition, a step list, or a rule-of-thumb with context.

Fundamental #2: Be the Best Page for the Whole Query

Examples:

Instead of “Best trail running shoes,” one canonical guide includes: terrain types, cushioning tradeoffs, sizing, injury considerations, and maintenance—so the page supports multiple sub-questions.
A SaaS pricing page includes FAQ about contracts, data security, implementation time, and integrations—common fan-out branches.
A recipe page includes substitutions, storage, and altitude adjustments—common follow-ups AI systems look for.

Do next:

Consolidate overlapping articles into one canonical, deeply useful resource page where it improves completeness (and reduce internal competition).

Fundamental #3: Technical Accessibility Is the Gate

If Google can’t reliably crawl, render, and understand your pages, you won’t be cited. Google continues to emphasize technical soundness and clear architecture [55].

Examples:

A fast mobile site with stable layout, compressed media, and minimal render-blocking scripts keeps users engaged and reduces crawler friction.
Semantic HTML (real headings, lists, tables) helps systems interpret structure without guessing.
Clean internal linking ensures key pages are discoverable and regularly recrawled.

Do next:

Audit JavaScript-heavy templates: ensure core content and links are present in rendered HTML and not gated behind interactions.
Keep key pages lightweight; Google’s guidance warns against bloated experiences and emphasizes usability [55].

Fundamental #4: Use Structured Data for Clarity (Not as a Hack)

Structured-data use cases that help:

Product schema for ecommerce pages (price, availability, SKU) aligned with visible content.
Organization schema with sameAs links to official profiles to help entity disambiguation (when appropriate and accurate).
FAQPage schema only when FAQs are visible and useful (not spam).

Do next:

Validate with Google’s tools and fix warnings; “almost correct” markup often yields “almost no benefit” [68].

Fundamental #5: Refresh, Monitor, and Iterate

Examples:

If a page earns AI citations but low CTR, improve snippet alignment: tighten the opening answer, add a clearer value promise, ensure the cited section matches the title.
If AI impressions rise but conversions don’t, adjust the landing experience: faster page, clearer next step, better comparison table, stronger trust elements.
If competitor brands are cited for “best option” lists, publish your own data-backed comparison criteria (not fluff) and earn relevance.

Do next:

Set a monthly AI visibility review cadence (more on metrics in Step 4).

Step 3 — Run the “Non-Commodity Test” (Content-Quality Audit for AI Citation)

Use this practical non-commodity test to audit priority pages (start with top revenue categories and top “how-to” content).

The Non-Commodity Test (Score Each 0–2)

Firsthand input present? Do you include original photos/videos, measured results, real workflows, or expert review—something that proves experience?
Decision utility? Does the page help a user choose/act (rules, checklists, tradeoffs), or only describe?
Specificity and constraints? Does it address “it depends” scenarios, edge cases, and exclusions?
Evidence and references? Do you cite your own methodology, data, or authoritative sources (not random blogs)?
Extractable clarity? Could one section be quoted as a complete, correct answer?

Examples

Running-shoe diagnostic video (non-commodity win): A page embeds a short video showing a gait test plus a table mapping outcomes (overpronation, neutral, supination) to shoe features and injury considerations. Add a brief “why this works” explanation and a disclaimer. That combination is both useful to humans and quotable for AI.
Commodity loss: “Top 10 running shoes in 2026” that rehashes specs from manufacturer pages without any testing method, comparison criteria, or user-fit logic. AI can synthesize this without citing you.
B2B non-commodity win: A “migration checklist” that includes a timeline template, stakeholder roles, common failure points, and screenshots from real implementations (sanitized). AI can cite a step list because it’s concrete and operational.

Do next:

Pick 10 pages for a two-week sprint. Upgrade them to pass the test by adding: a unique asset (video/table/template), decision logic, and a strong answer-first section.
Publish “small original data” consistently. Even simple internal benchmarks (with methodology) can differentiate you—uniqueness is a compounding advantage in AI citation eligibility [55].

Step 4 — Measure GEO with Google Search Console

What to Track (Three Tiers)

Visibility (leading indicators) — AI impressions: how often your pages appear as citations in AI features [86][93]. AI clicks / CTR: whether those citations drive visits.
Engagement (quality signals you control) — Landing-page engagement: scroll depth, time-on-page, key interactions. Page speed and mobile UX: remove friction that turns AI clicks into bounces [55].
Business impact (what leadership cares about) — Leads, trials, purchases, assisted conversions from AI-citation landings. Conversion rate by AI-traffic segment vs traditional organic.

How to Use GSC’s AI Impression Reports (Practical Workflow)

In GSC → Performance, add the Generative AI dimensions (where available) and compare: pages with AI impressions vs pages without; queries that trigger AI citations vs classic results.
Identify citation winners and reinforce them: improve the cited section for clarity and completeness; add internal links to high-converting next steps (pricing, category, demo).
Identify near-misses: pages ranking well organically but getting few AI impressions may lack extractable answers, structured clarity, or non-commodity depth. Apply the Non-Commodity Test (Step 3).

Two Measurement Examples

Ecommerce category page: AI impressions rise for “best stability shoe for knee pain,” but CTR is low. You add a quick decision table near the top, compress images for faster mobile load, and clarify sizing guidance. CTR improves and category PDP clicks increase.
B2B how-to guide: AI clicks grow, but conversions don’t. You add a “download checklist” CTA and a short comparison section linking to product pages. Conversions improve because the AI visitor now has a clear next action.

Do next:

Create a weekly “AI visibility standup”: 30 minutes reviewing AI impressions, AI CTR, top cited pages, and one content/tech fix to ship that week.
Tie AI reporting to revenue by adding UTM conventions (where possible) and segmenting in analytics: “GSC AI citation traffic” as its own channel group.

Step 5 — How Iriscale Supports Execution (Mapped to Google’s GEO Pillars)

Below is a direct mapping between Google’s GEO pillars and how Iriscale supports execution.

Pillar A: People-First, Unique, Helpful Content

Google’s guidance: Create high-quality, original, useful content; avoid gimmicks and unnecessary AI-specific tactics [55].

Example execution: Your running-shoe guide brief requires a diagnostic video + decision table + “who this is for” section—so the finished asset is inherently quotable.

Pillar B: Technical Accessibility and Clean Architecture

Google’s guidance: Maintain crawlability, indexability, and strong site experience; AI features still depend on core systems [55][4].

Example execution: Iriscale flags heavy category templates on mobile and prioritizes a compressed-media and render-path fix to protect both UX and AI-crawl reliability.

Pillar C: Structured Data for Clarity

Google’s guidance: Use JSON-LD, mark up visible primary content, validate implementation, and avoid spam [68].

Example execution: Product schema is deployed consistently across PDPs, validated, and monitored for drift when merchandising changes.

Pillar D: Refresh and Relevance

Google’s guidance: Keep content updated and aligned with evolving systems; focus on helpfulness through core updates [55].

Example execution: Quarterly refresh cycles for “best X” and “how to choose X” guides, with clear evidence updates and revised tables.

Pillar E: Measurement and Adaptation

Google’s guidance: Use Search Console’s generative AI performance reports to track AI visibility [86].

Example execution: A weekly report shows top cited URLs, top AI-triggering queries, and the next best action to increase citation share and downstream conversions.

Checklist: “Good SEO Is Good GEO” Action Template

Stop chasing AI-search hacks (no mass rewrites, no schema spam, no LLMs.txt reliance) [55][64]
Pick 10 priority pages (revenue + top informational) for AI Search Readiness upgrades
Add answer-first paragraphs and descriptive H2/H3 structure for extractability [55]
Pass the Non-Commodity Test: firsthand element, decision utility, constraints, evidence, clarity
Consolidate overlapping content into canonical “best page for the query + fan-out” resources
Ensure mobile performance and technical accessibility (crawl, render, internal links) [55]
Implement/validate structured data (JSON-LD, visible content only, test regularly) [68]
Set a monthly refresh cadence for citation-candidate pages
Turn on and review GSC Generative AI performance: AI impressions, clicks, CTR; segment winners/near-misses [86][93]
Tie AI visibility to business outcomes: landing-page engagement + conversions by AI segment

Ready to Operationalize Google-Approved GEO?

Related Guides

Sources

[1] https://www.digitalapplied.com/blog/google-vp-search-good-seo-is-good-geo-cmo-2026

[2] https://ppc.land/googles-vp-of-search-tells-cmos-good-seo-is-still-all-you-need-for-ai

[4] https://goo.gle/3PO7HfB

[49][86] https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports

[51] https://cloud.google.com/use-cases/retrieval-augmented-generation

[55] https://developers.google.com/search/blog/2026/05/a-new-resource-for-optimizing

[64] https://www.seroundtable.com/google-does-not-endorse-llms-txt-40789.html

[68] https://itxitpro.ae/blogs/structured-data-schema-for-ai-search-how-to-feed-the-machines-in-2026

[77] https://www.facebook.com/SearchEngineJournal/posts/googles-john-mueller-dismisses-llmstxt-as-speculative-for-now-and-says-he-likes-/1472670388236740

[93] https://support.google.com/webmasters/answer/16984139?hl=en

Good SEO Is Good GEO: Your Action Plan for AI Search Visibility

What You Need to Know

Step 1 — Cut Through the Acronym Confusion

Step 2 — Execute the 5 Fundamentals (Google-Validated)

Fundamental #1: Build Content Worth Citing (Not Just Ranking)

Fundamental #2: Be the Best Page for the Whole Query

Fundamental #3: Technical Accessibility Is the Gate

Fundamental #4: Use Structured Data for Clarity (Not as a Hack)

Fundamental #5: Refresh, Monitor, and Iterate

Step 3 — Run the “Non-Commodity Test” (Content-Quality Audit for AI Citation)

The Non-Commodity Test (Score Each 0–2)

Examples

Step 4 — Measure GEO with Google Search Console

What to Track (Three Tiers)

How to Use GSC’s AI Impression Reports (Practical Workflow)

Two Measurement Examples

Step 5 — How Iriscale Supports Execution (Mapped to Google’s GEO Pillars)

Pillar A: People-First, Unique, Helpful Content

Pillar B: Technical Accessibility and Clean Architecture

Pillar C: Structured Data for Clarity

Pillar D: Refresh and Relevance

Pillar E: Measurement and Adaptation

Checklist: “Good SEO Is Good GEO” Action Template

Related Questions

Is GEO different from SEO in Google Search?

Do we need to create an LLMs.txt file to appear in AI Overviews?

Does structured data increase AI citations?

What should we report to leadership: AI impressions or revenue?

Ready to Operationalize Google-Approved GEO?

Related Guides

Sources

Good SEO Is Good GEO: Your Action Plan for AI Search Visibility

What You Need to Know

Step 1 — Cut Through the Acronym Confusion

Step 2 — Execute the 5 Fundamentals (Google-Validated)

Fundamental #1: Build Content Worth Citing (Not Just Ranking)

Fundamental #2: Be the Best Page for the Whole Query

Fundamental #3: Technical Accessibility Is the Gate

Fundamental #4: Use Structured Data for Clarity (Not as a Hack)

Fundamental #5: Refresh, Monitor, and Iterate

Step 3 — Run the “Non-Commodity Test” (Content-Quality Audit for AI Citation)

The Non-Commodity Test (Score Each 0–2)

Examples

Step 4 — Measure GEO with Google Search Console

What to Track (Three Tiers)

How to Use GSC’s AI Impression Reports (Practical Workflow)

Two Measurement Examples

Step 5 — How Iriscale Supports Execution (Mapped to Google’s GEO Pillars)

Pillar A: People-First, Unique, Helpful Content

Pillar B: Technical Accessibility and Clean Architecture

Pillar C: Structured Data for Clarity

Pillar D: Refresh and Relevance

Pillar E: Measurement and Adaptation

Checklist: “Good SEO Is Good GEO” Action Template

Related Questions

Is GEO different from SEO in Google Search?

Do we need to create an LLMs.txt file to appear in AI Overviews?

Does structured data increase AI citations?

What should we report to leadership: AI impressions or revenue?

Ready to Operationalize Google-Approved GEO?

Related Guides

Sources