GEO vs SEO: What Changes in Your Budget

SEO optimises a URL so it ranks in a list and earns a click. GEO optimises an entity, your business, and the passages that describe it, so that an assistant retrieves it, trusts it and cites it inside a generated answer. Same raw material in many cases. Different unit of success.

The misread is treating GEO as a replacement discipline. It is not. It is a different scoreboard: inclusion and recommendation rather than position. You can hold the top organic spot and still be absent from the answer a buyer reads before they ever reach a results page, because assistants routinely cite pages that do not rank in the top ten for the same query.

Answer Engine Optimisation (AEO) sits almost entirely inside this. The vocabulary differs, the work overlaps heavily, and the distinction matters far less than the measurement model sitting underneath it. If you want the terms laid out plainly, our AI visibility glossary covers GEO, AEO and the related terms without asking you to learn a new acronym set.

One structural point before budgets. The comparison is not symmetrical. You run one SEO programme against one dominant search engine. You run one GEO programme against many assistants, and each weights sources differently. The same prompt in ChatGPT, Gemini and Perplexity can return different cited sources on the same day. So the phrase "we appear in AI search"means nothing until you name the assistant and the prompt.

Start here, because this is where budget gets wasted first. A large share of what makes a brand citable is work you already pay for.

Technical foundations. Crawlability, clean information architecture, sensible internal linking and page speed still gate retrieval. If a page cannot be fetched and parsed, it cannot be quoted.

Entity clarity. Consistent naming, category and service descriptions across your site, your directory listings and your third-party profiles. Most SEO teams already maintain some of this.

Transferable authority. Editorial mentions, review platforms, industry directories and trade press. Assistants pull from these sources constantly, and good digital PR has been building them for years.

The honest conclusion: roughly four fifths of GEO is SEO hygiene with a different purpose attached. Which is why hiring a separate generative engine optimisation agency before you have audited what you already have is usually premature. Buy the diagnosis first, not the retainer.

The remaining portion is real, and it is not covered by a standard retainer.

Passage-level structuring. Assistants lift passages out of context. A paragraph that only makes sense after the two above it will be dropped or paraphrased badly. Self-contained answers, defined terms, explicit qualifiers ("for UK businesses under 250 staff"), comparison tables and clear pricing statements survive extraction. Long scene-setting introductions do not.

Brand representation repair. This is the pillar most programmes fail at, and it has nothing to do with publishing. Assistants reconcile your website against directories, review sites, company records and old press coverage. A business described three different ways across those sources gets hedged, miscategorised, or quietly left out. We worked with DarkInvader, a cyber security business that assistants were placing in the wrong category entirely. Correcting how it was described on its own site and across third-party sources changed how it appeared in answers. No new content required. Fixing representation data frequently moves inclusion faster than anything you publish.

Citation mix building. Earn presence in the sources that actually appear in answers for your category, rather than chasing links by domain rating. And treat the mix as unstable. Reddit is the obvious case: its prominence in ChatGPT citations has swung sharply in both directions since 2024, so any plan built on a single source type ages badly. Track which third-party sources show up in your own prompt results each month, then rebalance. Do not copy a generic tactic from a blog post written two quarters ago.

Technical additions specific to AI retrieval. Structured data aligned to schema.org types that describe your organisation, services and pricing. The emerging llms.txt convention, which is worth implementing cheaply and not worth over-engineering while adoption is still uneven. And a deliberate decision about which AI crawlers you allow, taken at board level rather than by whoever edits robots.txt.

Freshness and consistency as standing tasks. Stale pricing, an old service area, a discontinued product still described as current. These are maintenance jobs, repeated, not a project with an end date.

Prompt sets replace keyword lists. The structural difference is worth showing side by side.

SEO: a list of head terms and long tails, tracked weekly in a rank tracker, reported as average position and estimated traffic.

GEO: 30 to 100 buying-intent questions ("best cyber security provider for UK fintech", "who should I use for industrial automation in Leeds", "is X or Y better for mid-market payroll"), re-run on a fixed cadence across several assistants, reported as inclusion and recommendation.

One ChatGPT check is not monitoring. Answers vary by session, by region, by account history and by day. A single look tells you what one assistant said once. Repeated runs tell you whether you are reliably in the answer, which is the only version a board should accept.

Multi-platform tracking matters more now that ChatGPT's share of AI referral traffic has come off its peak. Gemini, Perplexity, Claude and Meta AI each need their own prompt runs. Single-platform checking is the most common measurement error we see in in-house teams, usually because one assistant is the one the leadership team happens to use personally.

The metrics that replace rank:

Inclusion rate: how often you appear at all across the prompt set.

Recommendation position: whether you are named first, buried in a list, or mentioned only as an alternative.

Sentiment and category accuracy: is the assistant describing what you actually sell.

Share of voice against named competitors on the same prompts.

Which sources were cited, so you know what to go and influence.

We fold those into our six pillars (technical foundations, clarity, consistency, trust, visibility and freshness) and express them as a single AIRO score, so the same prompt set can be re-run and compared like for like. The score is a tracking device, not the KPI. The KPI sits in commercial reporting: AI Overviews click loss on affected query sets, assisted conversions from AI referrals, and branded search lift. Ahrefs published analysis in 2025 associating the presence of an AI Overview with a fall of roughly a third in clicks to the top-ranking page. That figure, applied to your own affected queries, is how you size the case before asking for budget.

Three realistic models

Fold it into the SEO retainer. Suits businesses with a competent agency or in-house team, a modest prompt set and no urgent representation problems. You add monitoring and reallocate a portion of existing hours. No new line item.

Monitoring subscription plus day-rate implementation. The common middle ground. You buy repeated prompt measurement, then buy implementation days against what it finds. Our AI visibility plans and pricing are built around this split.

A separate programme. Justified where AI referrals already convert, where the category is contested, or where the brand is being actively misdescribed. Regulated sectors and multi-territory brands land here most often.

A warning on model two. Monitoring without implementation stalls, every time. The dashboard tells you that you are absent from eleven prompts; nobody has the days booked to fix it. If you can only fund one, fund the implementation work and measure less often.

Ownership, which is where most programmes break

GEO budgets fail on ownership, not technique. The pillars split across teams: technical foundations and freshness sit naturally with SEO, clarity and consistency sit with brand, trust and visibility sit with PR and partnerships. Without one named owner holding the prompt set and the score, the work stops after the technical fixes, which are the easy fifth.

A 90-day sequence

Days 1 to 14: baseline prompt audit across assistants. Nothing else happens first. A before-and-after is the only credible proof in this field, and teams that skip the baseline can never show whether a change or a model update caused the shift.

Days 15 to 40: fix representation errors in directories, profiles and on-site descriptions.

Days 40 to 65: restructure priority pages for passage extraction and add structured data.

Days 65 to 85: build presence in the third-party sources your prompt results actually cite.

Day 90: re-run the identical prompt set, same cadence, same regions, and report the delta.

Governance questions procurement will ask

Because the method evaluates AI-generated outputs, governance is not optional. Expect questions on data handling, on alignment with the NIST AI Risk Management Framework, and on readiness against the ICO's guidance on AI and data protection. Have the answers written down before procurement asks, not after.

Rebuilding content that already performs instead of correcting the entity data assistants are reading. Expensive, slow, and it treats a consistency problem as a content problem.

Chasing one assistant because it is the one the CMO uses. Source weighting differs by platform. Optimising for one is a partial answer reported as a whole one.

Treating a single visibility score as the KPI with no competitor benchmark and no before-and-after. A number with nothing to compare it to is decoration.

Blocking AI crawlers to defend clicks. This is a trade-off, not a safety measure. If your competitors remain citable, the assistant still answers the question, just without you in it. Decide it at board level with the commercial numbers on the table.

Publishing high-volume AI-written content that adds nothing a model could already generate. Assistants have no reason to cite a restatement of what they already know.

If you want a starting read on where you currently stand, a free AI visibility scan will show whether you appear for a sample of category prompts. If the answer is uncomfortable, that is the budget case, already written.

Is GEO replacing SEO, or do I need both?

You need both, and they share most of their foundations. SEO still determines whether your pages can be crawled, parsed and retrieved, which is a precondition for being cited. GEO changes the unit of success from position to inclusion and recommendation inside a generated answer.

How much of my existing SEO work already helps with GEO?

In our experience the majority of it does: technical health, internal linking, consistent service descriptions and earned coverage in credible third-party sources all transfer. The genuinely new portion is passage-level structuring, representation repair across directories and profiles, AI-specific technical work, and repeated prompt measurement. That is why we recommend auditing before appointing a new supplier.

What should I measure for GEO if there are no keyword rankings?

Measure inclusion rate across a fixed prompt set, recommendation position within answers, category accuracy and sentiment, share of voice against named competitors, and which sources the assistants cited. Connect those to commercial reporting through AI Overviews click loss, assisted conversions and branded search lift. One assistant checked once is not measurement.

Do I need a separate generative engine optimisation agency or can my SEO team do it?

A capable SEO team can own the technical and freshness pillars without outside help. The parts that usually stall are consistency across third-party sources and multi-platform prompt monitoring, because those sit with brand and PR rather than SEO. Bring in a specialist for the baseline, the prompt set and the measurement cadence, then decide whether ongoing AI visibility monitoring or internal ownership makes more sense.

How long before GEO work changes how AI assistants describe my brand?

Representation fixes in widely-referenced third-party sources can show up within a few weeks, though timing varies by platform and by how often each assistant refreshes its sources. Structural content changes and newly earned citations generally take longer, which is why we re-measure on a fixed cadence rather than promising a date. No one can guarantee a specific result, and any supplier who does is overstating what the evidence supports.