AI search: where SEO, GEO and AEO fit

AI-assisted search is changing how people explore questions and compare information. For a business, the practical challenge is deciding which work improves its website and which ideas are only attractive labels. AI SEO, generative engine optimization and answer engine optimization are commonly used terms for work around discovery and answers. They should not be treated as a promise of a particular citation or ranking. This guide separates established Google Search guidance from a practical content-review approach. It does not assume that every AI service uses the same rules. The aim is a website that offers accessible, useful and defensible information as discovery experiences evolve.

Define the outcome before choosing a tactic

Begin with the audience and the commercial question. Do you want potential clients to understand your service, discover a product or find accurate information about your business? Identify the pages that support that goal and the evidence you can measure. A request to appear everywhere in AI answers is too vague to guide useful work. A more practical brief names the topic, audience, target market and intended next action. Then distinguish what you control, such as page quality and access, from what a platform controls, such as whether it chooses to include a link. This prevents visibility discussions from becoming guarantees.

Understand what Google's guidance actually says

Google states that established SEO practices remain relevant to AI Overviews and AI Mode. A supporting page needs to be indexed and eligible for a search snippet, and Google does not require special AI markup or a new AI text file for those features. Meeting requirements does not guarantee inclusion. This guidance provides a useful boundary: first investigate ordinary access and content problems rather than assuming that a new format will solve them. Keep claims about other services separate. If someone proposes a platform-specific tactic, ask which platform it concerns, what evidence supports it and how its effect will be assessed.

Review your important pages as information sources

Take the pages associated with your main products or services and read them as if you knew nothing about the business. Can you identify what is offered, who it is for and what an enquiry involves? Are technical terms explained where needed? Are claims supported by visible evidence? This review is valuable independently of AI discovery. It can reveal outdated services, unclear delivery boundaries and inconsistent business details. Record the gaps in plain language. A recommendation such as explain the audit deliverables is easier to implement than a broad instruction to optimize for AI without identifying what is missing.

Organize answers without stripping away context

A concise answer can help someone orient themselves, but difficult questions still need explanation. State the main answer near the relevant heading, then explain conditions, tradeoffs and exceptions. For example, whether a website needs a migration depends on the technical problem and business requirement; a universal yes or no would be misleading. Use FAQs for genuine follow-up questions instead of repeating the article with slightly different wording. Keep the answer visible and understandable to a reader. The structure is an editorial recommendation for clarity, not a guarantee that an AI system will extract or cite a particular paragraph.

Show evidence that belongs to your business

If you describe a service, show its scope and the process a potential client can expect. If you discuss a result, identify the actual project, metric and comparison period using evidence you are allowed to share. Do not manufacture dashboards or ranking improvements to make a page look authoritative. Where no result evidence exists, explain the approach rather than presenting a fictional success story. Use real contact details and consistent descriptions of the business. This makes the website easier to evaluate. It also gives your content team a reliable set of facts to reuse across pages instead of making unsupported additions.

Keep access and structured information consistent

Technical review remains part of the work. Confirm that important pages open while logged out, that useful content is available in text and that navigation reaches the intended destinations. If you use structured data, check it against what a visitor can actually see. Avoid adding claims to markup that do not appear on the page. Review content after changes to a theme, page builder or caching system, since a correct draft can still produce a broken live page. Coordinate changes with the person responsible for development. A clear implementation note should describe the affected page, evidence and expected behaviour.

Use AI tools without surrendering quality control

A generated draft is a starting point, not verification. Ask a reviewer to check technical statements, dates, source references and the business facts used in examples. Avoid publishing many near-identical articles simply because they are inexpensive to produce. Google's guidance on generative content emphasizes quality, accuracy and relevance, and warns about scaled production without added value. Practical review also includes tone and usefulness. Does the page sound like your business? Does it answer something a customer would actually ask? Keep a source log and make the final editor responsible for resolving uncertainty before publication.

Measure discovery alongside meaningful actions

Google includes traffic from its AI search features within Search Console's Web performance data. That does not automatically provide a separate, complete view of every AI interaction. Build a measurement plan around available evidence: landing pages, relevant queries, recorded enquiries and conversions where tracking is configured correctly. For other services, document the reports or referral information available instead of assuming identical reporting. When manually observing an answer, record the platform, question, date and circumstances. Treat that observation as a snapshot, not proof of persistent visibility. Separate verified results from hypotheses about why a change may have helped.

Turn the review into an achievable work plan

Choose a small group of important pages and list the changes needed to make them clearer, more accurate and easier to use. Assign an owner to each task and record the evidence supporting it. Validate the live result after implementation, including navigation and mobile presentation. Agree on a follow-up review using measurements you can genuinely access. This sequence gives the business a concrete plan without pretending to control a third-party answer system. AI search work should remain accountable to the website and its customers. Start with a focused assessment, strengthen the foundations and evaluate outcomes honestly as platforms continue to change.

Example: evaluating an AI visibility proposal

Suppose a proposal promises that adding a special file will guarantee your brand appears in AI answers. Ask for the exact platform, the official requirement or reproducible evidence, and a definition of the promised result. Compare the proposal with the platform's published guidance. For Google's AI features, do not confuse an optional experiment with an established eligibility requirement. A better brief identifies existing page gaps, useful supporting information and a transparent measurement method. Keep experiments bounded and record what changed. This lets you explore new discovery opportunities while distinguishing a testable idea from a claim the supplier cannot substantiate.

References: AI features · Generative AI content · Technical requirements