AI search optimization: what to fix and how to measure it

What is AI search optimization?

AI search optimization is the work of making useful, accurate information about your business available to search systems and checking how it appears in generated answers. The practical goal might be a correct product description, a relevant recommendation, a source citation, or a useful visit. Specify the goal before measuring it.

You will also see GEO, or generative engine optimization, and AEO, or answer engine optimization. Their meanings overlap. Here, they describe work on visibility in AI answers. Using AI to write SEO articles is a different activity.

When an AI system searches the web, it can retrieve pages and build its answer from them. Google says its AI features may run several related searches across subtopics. That is why the sources behind an answer can differ from the usual top results for one short keyword. Google does not publish a formula for choosing those sources. Google’s explanation of AI features.

For your team, distinguish four observations:

A citation to a useful article is not a recommendation of its publisher as a supplier. A brand mention without a link still counts as a mention. Record both distinctions.

A current answer turned into a page brief

On October 22, 2026, at 21:29 UTC, we collected one US English Google AI Overview for “how to choose an MSP for a small business.” An MSP, or managed service provider, is an outsourced IT provider. We requested desktop results through DataForSEO.

The opening organized the decision around service scope, response times, security, and contract terms. Source links in the returned answer included provider guides and a Reddit discussion about finding an MSP. This was advice on evaluating a provider, rather than a ranked shortlist of suppliers.

That changes the task. For a question like this, the useful contribution is a clear explanation of what to compare. Treating every supplier the answer didn’t name as a lost recommendation would misread it.

The brief is our editorial conclusion from one observation. We did not edit an MSP’s website or measure a later change in citations. The next sections show how to run that work on your own site.

What live answers reveal in three US markets

We collected examples in three industries we work in: B2B software, real estate, and travel. The Google examples are US English AI Overviews. The GPT examples are fresh GPT-6.1 Sol responses, obtained through DataForSEO with web search on and the search country set to the United States. They are API responses, which can differ from what people see in the ChatGPT app.

The images rebuild selected passages in our own layout and keep the captured English wording. Short Google queries and longer API prompts differ, so treat this as a look at real buying situations, not a controlled comparison or a visibility score. Read the complete prompts, answers, and source links.

B2B software: brokerage CRM is a fit question

Google query: “best CRM for real estate brokerages in the United States.” The answer named Follow Up Boss, Lofty and Wise Agent among its options, attaching different reasons for choosing them. Its introduction framed the choice around brokerage size, lead volume, and a standalone CRM versus a platform with a website and marketing tools.

The API response also named Follow Up Boss, Wise Agent and Lofty. Its visible citations included suppliers’ own pages such as Follow Up Boss pricing, whereas the Google excerpt drew on comparison material from Kee Technology and Guideflow.

What we would change on a software website. “CRM for real estate” is only a starting point. The page also needs to explain how a brokerage receives and assigns leads, which staff roles it supports, how it connects to an existing property-search website, and where its limits are. A comparison page should judge every product on the same criteria and link to current product evidence. The source types above are what we observed, not proof of what caused inclusion. This is the page architecture and evidence work covered by our SaaS SEO service.

Real estate: the buyer needs a comparison they can inspect

Google query: “how to compare new construction condos in Miami.” Its headings covered area definitions, layouts, the developer’s record, and delivery and ownership costs. The returned links included broker-published material such as CondoBlackBook and a David Siddons Group comparison of three named developments.

The API response organized the same buying situation around comparable value, ownership costs, documents, and contract and delivery questions. Its citations included the Consumer Financial Protection Bureau and Florida’s condominium statute, alongside local market material. That tells us where this answer sent a reader to inspect a claim. It does not validate every financial or legal statement the model made.

What we would change on a developer or brokerage website. Let a buyer compare actual units or projects: what each area measurement includes, available plans, construction stage, the date facts were checked, and where the current documents can be obtained. Keep a project’s marketing description connected to its evidence and inquiry route. A glossy image cannot answer those questions on its own. Our real estate marketing work connects search entry pages, project information, and the route to an inquiry.

Travel: a kids’ club may not meet the buyer’s requirement

Google query: “how to choose a family friendly luxury resort in California with a kids club.” The answer raised age, schedules, and the difference between supervised drop-off care and a play space where a parent stays. One cited Tripadvisor thread is more than a decade old. It shows what travelers said then, not current hotel policy.

The API response explicitly distinguished a supervised club from family activities and an unstaffed room. It treated Four Seasons Residence Club Aviara as a counterexample for a family requiring supervised drop-off care. The official amenities page states that the Kids Club is not staffed and lists babysitting separately. That is a more useful distinction than simply counting whether a hotel name appears.

What we would change on a hotel or travel website. A family page should state eligible ages, dates and hours, whether staff supervise the children, how places are reserved, and when the hotel confirmed those details. Separate an on-site playroom from a childcare booking. For an agency, the inquiry should collect the children’s ages and travel dates so suitability can be checked. This page-and-source work is part of AI search optimization. It gives the buyer and a search system more precise information to work with, without promising a recommendation.

Start with your own market. Our free search potential map includes an AI snapshot on five buyer questions. Send your website, one priority product or service, and your target market. Get a free map.

How to optimize for AI search: a six-step workflow

1. Choose a buyer decision

Start with one product or service and one market. Gather questions from sales conversations, on-site search, Search Console, and keyword research. Use search volume to understand Google demand. It does not measure how often the same question is asked in ChatGPT.

For the IT provider, a small question set could cover:

• Evaluation: How should a small business choose an MSP?
• Fit: Which providers support a business of our size, in our location?
• Comparison: What changes between outsourced and co-managed IT?
• Risk: What should we check in a support agreement?
• Brand accuracy: What services does this particular provider offer?

For each question, record what a satisfactory answer would contain and which page owns that information. A pricing page, service page, documentation page, and buying guide have different jobs. Create a new page when the reader needs a distinct answer, rather than a separate URL for every variation of a prompt.

2. Record a baseline you can repeat

Save the exact question, platform and visible mode, date and time, market and language, account state, location method, complete answer, and source URLs. Record unknown settings as unknown. A fresh conversation and a continued conversation are different conditions.

Include repeated observations. As a small diagnostic workload, five questions asked three times in each of two search interfaces produce 30 scheduled observations, or 15 per interface. This is a proposed starting sample, not a statistically representative measure of the market or a required platform standard.

Separate a valid answer without your brand, a search with no AI Overview, and a technical failure. They should not all become “zero visibility.” Keep the planned count, successful collections, and answer-bearing count visible.

You can use the downloadable observation worksheet and its short instructions. It includes fields for conditions, mentions, citations, factual errors, the page to fix, and the next action. The filled record contains the real MSP observation above. We did not assign it a target-brand score because no supplier was selected as the target of this collection.

3. Trace a specific fact to a specific source

Open the cited URL and find the passage supporting the relevant statement. Compare it with your current product documentation and offer. A citation is a lead to investigate, not proof that every sentence in the answer is correct.

Suppose an answer says your service is available only in one city. Your current service page says nationwide, but an older directory profile still names one office. The inconsistency is observable. Whether that profile caused the answer remains a hypothesis until you have stronger evidence.

Write the task precisely: “Update the obsolete service-area statement on this profile and make the service area explicit on the service page.” That gives someone a concrete correction, an owner, and a way to check completion.

4. Check access and eligibility

Verify the actual URL, redirects, preferred page address (the canonical), index status, and visible text. Check whether preview restrictions prevent Google from showing a search snippet. Check the relevant crawler against server and security logs when available. A successful visit in your browser does not prove that a crawler can reach the same content.

An accordion is not automatically an indexing problem. Its text might already be in the HTML. Conversely, an empty container that fills only after an unsupported interaction may leave important information unavailable. Inspect the delivered page before prescribing a redesign.

Use the platform-specific controls below. For problems across many pages, start with technical SEO. If the main uncertainty is which problems matter, an SEO audit can establish the priority.

5. Repair the answer and its evidence

Make the page useful for the decision you selected. Replace vague promises with the actual scope, conditions, limits, and evidence. Put the important answer where a person can find it. Use diagrams and screenshots when they explain something text alone makes difficult.

For product facts, start with documentation. For an outcome claim, use evidence of that outcome, including its context and limitations. A named author does not turn an unsupported assertion into an established result.

External sources matter when they contain relevant information. Correct an outdated profile, contribute useful expertise, or document genuine customer experience. Do not invent reviews or manufacture “independent” corroboration. Our buyer-information study of Dubai residences shows another way to examine the questions a website leaves unanswered before an inquiry.

6. Repeat, compare, and choose the next action

Keep a change log with the URL, exact change, publication date, and available evidence of recrawling or indexing. Then repeat the same question set under the recorded conditions. If the platform or model changes, mark the break in comparability.

Compare factual accuracy, mentions and citations first. Review visits and inquiries as separate outcomes. A changed answer after an edit is an observation, not automatic proof that the edit caused it. Unchanged answers can also be useful: they may point to another source, insufficient recrawling, a poorly matched question, or a hypothesis that needs reconsidering.

AI crawler access and training controls

“Allow AI bots” is too broad to be an implementation instruction. Choose the platform and purpose.

Google’s Search generative AI setting is Include by default. It does not require every site to register manually. Check it if someone has changed the property or a parent property. Google’s setting documentation.

Google states that Google-Extended does not affect inclusion or ranking in Search. It is also a robots.txt control token, rather than a separate HTTP user-agent string to find in your logs. Google’s crawler documentation.

OpenAI documents search and training choices independently. You can permit OAI-SearchBot while disallowing GPTBot. Blocking the search crawler can still leave navigational links visible. ChatGPT-User is not the control that determines search eligibility. OpenAI’s crawler documentation.

If a firewall blocks a verified crawler, ask the site owner why that rule exists before changing it. One blocked request is not a reason to open the site to every bot or switch off its protection.

A service page before and after

Consider a hypothetical IT provider whose page opens with:

Proactive IT. Peace of mind. Your trusted partner for a changing world.

The tone may suit the brand. But it doesn’t tell a buyer who the service is for, what is included, or what happens when something breaks.

Here is a more useful opening for a fictional provider with the stated scope:

Managed IT for businesses with 20–80 employees

We manage Microsoft 365, employee devices, updates, and help desk requests. We can take responsibility for daily IT support or work alongside your internal IT lead.

Before you choose a plan, we review your current systems. Your proposal lists support hours, response commitments by issue priority, included work, additional charges, and the steps for moving from your current provider.

The audience and scope now give a buyer something to compare. The page should then link to the relevant service details, security responsibilities, onboarding process, and current pricing basis. Where a detail is confidential or depends on the project, explain what is provided during the next conversation.

Do not fill the gaps with invented specificity. If the business has no defined response commitment, the first task belongs to the service owner. A copywriter cannot create one by putting “15-minute response” on the page.

This is also where internal links earn their place. A buying guide explains how to choose, a service page explains the offer, and documentation backs up a technical claim. Connect them with descriptive links. Repeating a sales anchor in every paragraph doesn’t make the explanation more useful.

How to measure AI search visibility and results

Each question needs its own record.

Read the current Google report correctly

Search Console now has a dedicated Generative AI performance report for Search. It reports impressions with page, country, device, and date views. These data also contribute to overall Web performance. The documented impressions concern links to your site, rather than every unlinked brand mention. Do not interpret them as leads. Current report documentation.

If the report is missing, check the documented availability conditions, including sufficient impressions and the property’s participation setting. Its absence is not evidence that the site has never been mentioned in an AI answer.

Give a sample metric a denominator

For a hypothetical platform sample with 15 collected, answer-bearing observations, a brand named in 6 has a 40% mention rate in that sample: 6 ÷ 15. If its domain is cited in 3, its sample citation rate is 20%: 3 ÷ 15. These are different measures. Neither percentage is the company’s share of the entire AI search market.

Keep a no-AI-answer rate and collection failures beside those numbers. Otherwise, a change in how often an answer appears can hide behind a seemingly improved citation rate. Compare like-for-like question sets and report changes by platform before combining anything.

Connect the visit to a useful action

Review the source information actually captured by your analytics, not an assumed label. Someone can read an answer and return later through another channel. You will not recover every such journey from referral reports.

Agree with sales what counts as a relevant inquiry, preserve the landing page and available acquisition data, and follow the opportunity through the CRM. Our MQL vs SQL guide explains the qualification distinction. Connecting these records lets you examine which recorded visits become suitable inquiries and sales opportunities.

When a paid visibility tool is useful

You can begin with a browser, the observation worksheet, Search Console, site analytics, and CRM records. A visibility platform becomes useful when collecting and checking the same questions across markets and systems takes too much manual work.

Before paying, check whether it exports complete answers, source URLs, market and mode settings, timestamps, failed runs, and the definition behind each score. A dashboard that cannot show its underlying observations is difficult to audit. A tool’s prompt sample is also not a census of everything buyers ask.

What to fix first

Start with a problem you can demonstrate and that affects an important buying decision.

Choose the smallest complete correction, record it, and check the result. If the page is already accurate and useful, investigate the question and source set before rewriting it.

For a business starting from scratch, the first useful deliverable is a small baseline: one priority offer, a buyer question set, saved observations, the pages and facts that need work, and a reasoned order of action. That is the foundation of our AI search optimization work.

AI search advice worth questioning

Google’s current optimization guide rejects several supposed prerequisites: special AI files such as llms.txt, special schema, tiny content chunks, and a fixed page length. Its warning about scaled pages also matters when someone proposes a URL for every prompt variant.

Use a short answer, meaningful headings, tables, and images because they help the reader find and inspect information. Those editorial choices do not constitute a guaranteed citation format.

Other useful checks before approving a recommendation:

• “You must rank in the top ten first.” Ask for the platform’s actual requirement. Google documents indexing and snippet eligibility, not a universal top-ten prerequisite.
• “This schema will get you cited.” Ask whether the evidence shows a causal effect or a correlation, and whether it applies to the platform and page type involved.
• “We increased visibility, so revenue increased.” Ask for the separate visit, qualification, and sales records.
• “Every AI platform works this way.” Ask which system, mode, market, date, and evidence support the advice.
• “You should see results in 30 days.” Separate a delivery deadline for the work from an unsupported deadline for a third-party answer to change.

Sources and method

Sources checked: October 23, 2026. Platform guidance can change. This article separates documented controls, a dated observation, and our proposed diagnostic workflow.

The MSP example uses one Google Organic AI Overview collected through DataForSEO Live with US location code 2840, English, and a desktop request. The provider timestamp is October 22, 2026, 21:29:35 UTC. It is not a before-and-after experiment. The fictional page rewrite and the sample metric arithmetic illustrate the method and do not report client outcomes.

The three industry examples add three Google AI Overviews and three GPT-6.1 Sol API responses with US web search enabled. Their images are branded reconstructions, not native interface captures. We retained an original resort query with no AI Overview, a Miami provider error followed by one successful retry, and a rejected API parameter with no charge before the corrected request. These outcomes are collection records, not measures of platform quality. Exact prompts, dates and complete outputs are in the US example record.