Record the AI answer and the conditions that produced it. Trace the sources behind it. Fix crawl access, index eligibility and the page that owns each subject. Support important claims with evidence you have permission to use. Then repeat the check under the same conditions. Google and OpenAI guidance supports this foundation, but neither promises inclusion.
The Google and OpenAI guidance reviewed for this method gives that work a clear foundation: search accessibility, useful original information and evidence that readers can inspect. It also establishes a limit. Making a page eligible does not secure its appearance in an answer.
What the primary sources support
Google keeps AI Overviews and AI Mode within the search foundation. A page needs to be indexed and eligible for a search snippet. Crawl access, internal links, page experience, visible text, useful content and accurate structured data remain relevant. Following this guidance does not guarantee crawling, indexing, inclusion, ranking or a stable appearance in an AI feature.
Google’s generative AI guidance also distinguishes useful original content from interchangeable pages created to target query variations. The editorial question is what a page adds: a clear explanation, documentation, a comparison or evidence the reader could not obtain from another generic description.
OpenAI says public websites can appear in ChatGPT search. Access for OAI-SearchBot affects discovery, surfacing, citations and inclusion in summaries or snippets. Crawler access is therefore a technical check, with appearance and referral traffic measured separately afterward.
| Primary source | What it supports in this method | Checked |
|---|---|---|
| Google: AI features and your website | SEO fundamentals, eligibility requirements and the limits of inclusion | 1 Sep 2026 |
| Google: Optimizing your website for generative AI features on Google Search | Clear technical structure, unique content and the warning against manipulative page volume | 1 Sep 2026 |
| OpenAI: Publishers and Developers FAQ | Public-site eligibility, OAI-SearchBot access, citation and snippet language, referral measurement | 1 Sep 2026 |
A six-part method
Define the buyer decision
Choose questions about the product, category, alternatives, objections and proof that could affect a considered purchase. This gives the observation a commercial purpose. A generic prompt list can produce plenty of answers without explaining which one matters to the buyer.
Record the observation conditions
Save the system, model or mode when visible, account state, market, language, exact prompt, date, answer, cited sources and important uncertainty. Those details make a later comparison interpretable. Without them, a changed answer could reflect a different setup rather than a change in the company’s visibility.
Trace the sources behind the answer
Compare the answer with search results, the company website, product pages, documentation, profiles, independent coverage and the language used in the category. Locate the specific fact or explanation that needs attention. Keep what the system visibly cites distinct from other sources you inspect during the investigation.
Fix access and clarity
Check crawl rules, index eligibility, internal discovery and visible text. Confirm which page owns each important subject, whether product and category descriptions agree, and whether a reader can find a clear answer. This turns an observation into an identifiable page or technical task.
Support the claim
Connect important statements to product facts, transparent research, documentation, named expert responsibility and external evidence the company has permission to use. If a claim cannot be supported, remove or narrow it. Manufactured reviews or third-party corroboration create another factual problem for the next review.
Repeat the observation
Repeat the check under recorded conditions and compare the answer, representation of the brand and cited sources. Keep that record beside the changes made. It shows what changed in the answer without assigning every change to the work automatically.
What we see in practice
On 5 September 2026 we recorded one Google AI Overview in the United States for “how to choose an MSP for a small business”. MSP stands for managed service provider, an outsourced IT partner. The results were not personalized.
“Evaluate providers based on your specific operational needs, cybersecurity standards, and transparent pricing rather than just marketing claims.”
The answer organized the choice around four buyer tasks: the scope of services, cybersecurity and compliance, response commitments, and pricing with contract terms. Its visible links went to a specialist blog, an insurer’s guidance, an MSP directory’s guide and a practitioner discussion on Reddit. Provider pages were only part of the evidence.
| Buyer question | What the page should explain | Evidence to collect |
|---|---|---|
| What does the service cover? | Services, supported systems, onboarding, and the work covered by each plan | The current service specification and plan details |
| Can this provider meet our security requirements? | Security responsibilities, relevant credentials, and how the provider handles incidents | Current credentials and a documented incident process |
| When will someone actually respond? | Support hours, response commitments, and the route for urgent requests | The service-level agreement and escalation procedure |
| What are we committing to? | Pricing, plan inclusions, additional charges, and contract terms | The approved offer and contract terms |
Source: Cloud Tango, Travelers Insurance, mspcompanies.us, Reddit r/msp, links visible in the answer on 5 September 2026.
This covers steps 1 and 3 of the method on one query: the buyer decision sets the page brief, and the sources show which evidence shapes the comparison. The observation is a dated snapshot. It shows what appeared and which links Google attached. It does not show what caused inclusion, or that the answer will stay the same.
How to read the result
Answer visibility, citations, referral traffic, inquiries, qualified opportunities, pipeline and revenue describe different stages. Record each separately.
- Answer visibility
- Citations
- Referral traffic
- Inquiries
- Qualified opportunities
- Pipeline
- Revenue
A visibility score cannot establish that people visited, that sales accepted the inquiries or that revenue followed.
The method therefore avoids guarantees of model mentions, citations, rankings, leads or revenue. It also excludes large volumes of interchangeable prompt-targeted pages and invented reviews, citations or expert evidence.
Sources and review
- Primary sources
- Google Search Central and the OpenAI Help Center, listed below.
- Checked
- 1 September 2026. Guidance changes. The date shows when each page was last read.
- What this article does not claim
- That any step guarantees mentions, citations, rankings, leads or revenue.
Sezemov, M. (2026). AI search optimization: what to check, change and measure. Wonderflow. wonderflow.agency/insights/ai-search-optimization-without-the-folklore/
