SEO vs GEO: what businesses need to understand

Define the work before debating the acronym

For this article, SEO means improving a website’s ability to be discovered and understood through search, while serving the people who arrive. GEO, short for generative engine optimisation, is a label we will use for work intended to improve visibility in generated answers. Those definitions help organise a discussion; they should not be mistaken for a single technical standard adopted by every platform.

The useful commercial question is not which acronym will replace the other. It is what your supplier proposes to change, which system the change is intended to affect and how success will be assessed. A proposal to fix indexing, clarify service pages and review crawler permissions can be evaluated. A promise to make AI recommend your business needs much harder scrutiny.

Google explicitly says established SEO best practices remain relevant to AI Overviews and AI Mode. It says there are no additional requirements or special optimisations needed to appear in those experiences. Any proposal claiming that Google requires an entirely separate GEO layer should be checked against that primary guidance. ^[1]

Understand where the foundations overlap

Google’s guidance for its AI features includes allowing crawling, making important content available in text, linking internally and providing a good page experience. It also says structured data should match visible content. These are tangible review tasks. None depends on accepting a new marketing term, and none provides a guarantee of being selected for a generated answer. ^[1]

Content quality belongs in the same conversation. Google’s people-first guidance asks whether material provides original information, substantial coverage and clear evidence of relevant expertise. Use those questions to identify weak pages before commissioning a larger publishing programme. Producing more pages is not, by itself, an answer to missing substance on the pages that matter. ^[3]

Consider a hypothetical consultancy page that says only that its team delivers tailored solutions. A worthwhile improvement would explain the problem addressed, the scope of the engagement, relevant constraints and the next step. That recommendation is useful whether the discovery route is a conventional result, an AI answer or a direct referral. It does not require a prediction about ranking.

Recognise the genuine platform differences

Overlap does not mean every search platform works identically. OpenAI documents a search crawler, OAI-SearchBot, separately from GPTBot, which relates to model training. The controls are independent. A business can permit search crawling while declining training crawling, so an instruction to unblock all AI bots is too crude to be a responsible default. ^[2]

For Google’s AI features, supporting pages must be indexed and eligible to appear in Search with a snippet. Google also describes query fan-out, where multiple related searches can be used to help develop a response. That explanation should discourage simplistic assumptions that one target phrase corresponds to one fixed answer or one guaranteed supporting source. ^[1]

Ask a supplier to name the platform, the documented requirement and the proposed implementation. If they recommend a change beyond published guidance, ask them to label it as a hypothesis. There is room for careful experimentation, but a plausible idea should not be presented as an established rule or a universal requirement.

Separate deliverables from promises

A sound scope of work describes things that can be delivered and checked. It might include an access audit, revisions to service pages, internal linking improvements and validation of relevant markup. Ask for before-and-after evidence for each task. Keep promised implementation outcomes distinct from hoped-for visibility outcomes, which depend on systems outside the supplier’s control.

Structured data is a useful example. Google says valid markup does not guarantee a rich result. Its AI guidance also says no special schema.org structured data is needed for AI features. A proposal that sells schema as a guaranteed route into AI answers therefore overstates what the official guidance supports. ^[4][1]

• Which pages and templates will change?
• Which published guidance supports each technical recommendation?
• What will count as a completed, tested deliverable?
• What requires an experiment rather than a confident claim?
• Which outcomes cannot responsibly be guaranteed?

Use those questions during procurement, not only after a disappointing report. A supplier should be able to explain the work without relying on proprietary terminology. If a method cannot be described sufficiently for your team to approve its risks and assess its outputs, pause before authorising it.

Measure the business, not just a demonstration

Choose business measures before reviewing example AI answers. For a service business, you might track qualified enquiries, booked consultations or the suitability of incoming briefs. Define the measure precisely enough that a change in reporting cannot masquerade as a change in performance. Keep website changes and campaign dates in the same review log.

Google reports traffic from its AI features within the overall Web search reporting in Search Console. Do not assume that a general increase in reported search performance isolates an AI effect. Use the available data honestly and state where attribution is limited rather than constructing a confident explanation from an ambiguous total. ^[1]

If you sample AI answers, document the question, platform, date and observed sources. Treat the exercise as a reproducible observation, not a forecast of what every customer will see. Include questions that reflect genuine buying decisions and record absences as well as appearances. Avoid turning one favourable screenshot into evidence of sustained commercial impact.

A practical way to commission the work

SEO and GEO do not need to become rival budget lines. Start with a shared diagnosis, then identify any platform-specific access decisions or experiments. Prioritise work that is useful to prospective customers and can be checked after delivery. Keep experimental monitoring proportionate to the value of the decisions it will inform.

Before approving a proposal, use this checklist. It should leave you with a clear scope, documented assumptions and a realistic measurement plan. The aim is not to reject new discovery channels; it is to investigate them without abandoning the standards of evidence you would expect from any other business investment.

• Agree what SEO and GEO mean within the proposal.
• Fix material access and content problems first.
• Document separate search and training crawler decisions.
• Require testable deliverables and clearly labelled hypotheses.
• Track qualified business outcomes alongside visibility observations.
• Reject guarantees of rankings, citations or AI recommendations.