Day 118: Make Every Google AI Tactic Pass the Search Test
The easiest way to waste a Google AI budget is to buy a shortcut Google has not asked for.
A CMO hears a new proposal: create an AI text file, add special markup, restructure pages for AI Mode, expand schema solely for AI Overviews, or build a separate “AI SEO” layer before the site can be eligible for Google’s AI features. The proposal sounds plausible because the interface has changed. AI Overviews and AI Mode feel different from classic blue-link search results, so the procurement logic drifts: if the experience is new, perhaps the required technical layer must be new as well.
Google’s own guidance does not support that leap.
In its Search Central documentation on AI features and your website, Google says the best practices for SEO remain relevant for AI features in Search, including AI Overviews and AI Mode. It also says there are no additional requirements to appear in those features, nor other special optimisations necessary. To be eligible as a supporting link, a page must be indexed and eligible to be shown in Google Search with a snippet; Google states there are no additional technical requirements.
For CMOs, Marketing Directors, and founders, that creates a practical procurement test.
Before funding any Google AI tactic, ask whether it improves ordinary Search eligibility, usefulness, relevance, clarity, accessibility, or content quality. If it does, it may be worth doing. If it only exists because someone claims Google AI needs a separate mandatory layer, ask for the source.
Do not confuse a new interface with a new eligibility system
AI Overviews and AI Mode are not irrelevant to visibility strategy. They change how users explore questions, compare options, follow links, and encounter supporting sources inside Google Search. Google describes AI Mode as useful for nuanced questions, further exploration, reasoning, and complex comparisons. It also says AI Overviews and AI Mode may use query fan-out, issuing multiple related searches across subtopics and data sources to develop a response.
That matters for content strategy. A thin page that answers only one narrow keyword may not support a complex comparison well. A vague service page may not help a user understand fit, conditions, trade-offs, next steps, or limitations. A buried proof asset may be hard for Google-visible systems and human visitors to interpret. A page blocked from indexing, ineligible for a snippet, inaccessible, or low-value has a basic Search problem before it has an AI problem.
But that is different from saying Google has created a new technical admission ticket.
The procurement danger is the gap between those two statements. “AI features reward useful, accessible, high-quality Search content” is a defensible direction. “You need a separate AI file, special AI markup, or special schema to be eligible for Google AI visibility” is a different claim. Google’s guidance gives buyers a way to separate them.
The test is simple: if the recommendation would be sensible for Search users even without the phrase “AI Overview” attached, it is more likely to be legitimate. If the recommendation becomes valuable only because it is framed as a mandatory Google AI switch, it needs much stronger evidence.
Use claim, guidance, implication
A useful buying review does not need to become a technical argument. It can use three columns: the supplier claim, Google’s published guidance, and the procurement implication.
| Supplier claim | Google guidance to check | Procurement implication |
|---|---|---|
| “You need a new AI text file for Google AI Overviews.” | Google says you do not need to create new machine-readable files, AI text files, or markup to appear in these features. | Do not fund it as a required Google control. If there is a separate non-Google experiment, label and price it separately. |
| “AI Mode needs special schema.org structured data.” | Google says there is no special schema.org structured data you need to add for these features. Existing structured data can still be useful where it matches normal Search documentation. | Fund structured data where it supports eligible Search features or clearer page meaning, not as a claimed AI Mode admission ticket. |
| “Technical optimisation will guarantee inclusion.” | Google says meeting requirements, best practices, and policies does not guarantee that Google will crawl, index, or serve content. | Replace guarantees with observable deliverables: indexing checks, snippet eligibility review, content-quality improvements, and limitation notes. |
| “AI feature performance needs a separate reporting layer inside Google.” | Google says sites appearing in AI features are included in overall Search traffic in Search Console, particularly in the Performance report under the Web search type. | Be careful with attribution claims. Measure Search traffic and on-site quality, but do not buy invented precision. |
| “To opt out or control AI features, use a new AI-specific block.” | Google points site owners to Search controls such as Googlebot access, nosnippet, data-nosnippet, max-snippet, and noindex for limiting information shown from pages in Search. | Treat content controls as Search controls with trade-offs, not as a magic visibility dial. |
This table does not make every proposed tactic bad. It makes the required-versus-optional label harder to fake.
A machine-readable export may still be useful for another surface, internal reuse, partner enablement, site documentation, or a deliberately bounded experiment. Structured data may still help Google understand pages and qualify for Search features when it follows the relevant documentation. Technical SEO still matters. Accessibility still matters. Internal links, page experience, image and video best practices, merchant feeds, business profiles, and Search Console verification can all belong in a serious programme.
The issue is not whether the work can have value somewhere. The issue is whether the buyer is being told it is necessary for Google AI inclusion when Google has not made that claim.
Fund Search quality before AI theatre
The strongest Google AI work often looks disappointingly ordinary in a proposal.
It asks whether important pages can be crawled and indexed. It checks whether they are eligible for snippets. It reviews whether the content is helpful, reliable, people-first, current, specific, and clear enough for a real user. It improves titles, headings, internal links, navigation, accessible media, page experience, structured data where appropriate, and the substance of the answer the page gives. It verifies Search Console. It distinguishes observed traffic from inferred AI exposure. It preserves limits.
That can feel less exciting than a new AI layer. It is also much easier to defend.
For a CMO, the useful question is not, “What is the most AI-looking tactic we can buy?” It is, “Which Search and user-value problem would make this page a weak supporting source even if Google’s interface changed again tomorrow?”
A product page that does not explain who the offer is for, what problem it solves, which conditions change fit, what evidence supports it, and what next step a buyer should take is not fixed by an AI file. A comparison page that overclaims, hides trade-offs, or collapses distinct buyer routes is not fixed by special markup. A blocked or thin page does not become a better candidate for any Search experience because it has been wrapped in AI vocabulary.
The boring work is not conservative. It is the part Google’s guidance actually leaves buyers able to inspect.
Keep cross-agent experiments honest
The hard part is that Google is not the whole market.
ChatGPT, Claude, Perplexity, Gemini outside Google Search, specialist agents, partner systems, internal copilots, search results, directories, review sites, and scraped public pages do not all behave like one surface. A company may reasonably test machine-readable exports, llms.txt, cleaner documentation hubs, feed-like source pages, API-accessible reference material, or partner-ready summaries for non-Google discovery and reuse.
Those experiments should not be mocked. They should be labelled correctly.
“Optional cross-agent experiment” is a different procurement category from “required for Google AI visibility.” The first can be scoped with a hypothesis, target surface, measurement limit, cost cap, and stop condition. The second needs current Google support. When a supplier blends the two, the buyer loses control of the budget.
A clean brief might say:
- core Search work: indexing, snippet eligibility, content usefulness, internal links, accessibility, page experience, and Search Console checks;
- Google AI caveat: no additional AI Overview or AI Mode technical requirements claimed beyond Google’s published guidance;
- optional non-Google experiment: machine-readable export or llms.txt tested for defined non-Google discovery or agent-readability purposes;
- claim limit: no guaranteed inclusion, citation, ranking, traffic, attribution, revenue, or buyer behaviour.
That separation protects both sides. The buyer can fund useful experiments without being frightened into a false requirement. The supplier can explore emerging surfaces without pretending Google has endorsed the tactic.
The procurement sentence to put in the brief
Every Google AI proposal should have to survive one sentence:
If we removed the words “AI Overview” and “AI Mode”, would this work still improve Google Search eligibility, user value, accessibility, clarity, or content quality?
If the answer is yes, discuss priority, cost, and evidence. If the answer is no, ask whether the tactic is a separately labelled experiment for another surface. If it is neither, do not buy it as Google AI strategy.
That sentence is not anti-GEO. It is how GEO stays credible.
Generative Engine Optimization should help leadership understand how buyers encounter the business across answer-led and search-led surfaces. It should distinguish Google from ChatGPT, Claude, Perplexity, Gemini outside Google Search, and other systems. It should improve the public record where the company can responsibly do so. It should preserve caveats when the evidence is bounded.
What it should not do is rename ordinary fear as a mandatory technical add-on.
Google’s public guidance gives CMOs a useful source of discipline: no extra AI Overview or AI Mode requirements, no special AI text file, no special markup, no special schema.org requirement, and no guarantee of crawling, indexing, serving, ranking, or inclusion merely because a page follows best practice.
So make every tactic pass the Search test first.
If it helps a user and makes the page a stronger Google Search result, it may deserve budget.
If it only sounds necessary because the interface now says AI, keep the money in your pocket until the claim has a source.