AEO vs GEO vs AI Visibility: Which Problem Should You Solve First?

GEO, or Generative Engine Optimisation, has a formal research origin. The KDD 2024 paper GEO: Generative Engine Optimization introduced GEO as a framework for improving content visibility in generative-engine responses. It was not defined solely as a method for earning citations.

Source: ACM KDD 2024 Research Track

AEO, or Answer Engine Optimisation, is used less consistently. Some practitioners reserve it for direct-answer environments and use GEO for generative answers and citations. Others use AEO as an umbrella covering optimisation for systems such as ChatGPT, Gemini and Perplexity.

For example, HubSpot explicitly notes that practitioners use the terms differently while adopting AEO as its broader umbrella term. Adobe uses a more explicit distinction between AEO for direct extraction and GEO for generative synthesis and citation.

Sources: HubSpot on AEO vs GEO and Adobe on AEO vs GEO

Those are useful practitioner frameworks. They are not an industry standard.

For a B2B team, the more useful question is therefore not:

Which definition of AEO or GEO is correct?

It is:

What problem are we trying to solve, and which type of work is justified by the evidence?

For the complete operating strategy after that diagnosis, see Kojable's AEO Strategy guide.

Kojable's canonical visibility framework makes the distinction explicit: visibility asks whether the company appears; representation asks what the answer says; answer alignment asks whether that representation agrees with verified reality and evidence.

A company can therefore have high AI visibility and poor answer alignment. It may appear frequently but be assigned to an outdated category, associated with the wrong audience or described without the capability that differentiates it.

The reverse can also happen. A company may appear relatively infrequently but be accurately represented when it does.

For the full measurement treatment, see AI Visibility: Measurement Framework for B2B Teams and AI Representation: What It Means and How to Measure It.

If a company already appears consistently but is represented inaccurately, trying to raise its mention rate does not address the diagnosed gap.

The full methodology, metric boundaries and limitations remain on the canonical Research page: Branded AI Visibility vs Market Visibility.

The table below is a triage framework, not a universal causal model.

AEO or GEO should not be treated as diagnoses. They are possible workstreams after diagnosis.

Sometimes the appropriate action is new content. Sometimes it is correcting a factual page, strengthening evidence, improving technical accessibility, resolving contradictory company information or pursuing a realistic third-party correction.

Sometimes the evidence says to collect more observations before changing anything.

Citation data are still useful. They can show that a source appeared, identify which domains recur and reveal differences between answer environments.

Kojable's cross-platform Research found that visible citation exposure behaved differently across ChatGPT, Gemini and Perplexity depending on platform and query context. The practical conclusion was that one pooled citation rate could conceal materially different patterns. The study also states that visible citation exposure is not direct evidence of retrieval, source authority, claim support, answer accuracy or commercial impact.

For the full study, see Retrieval Need and AI Citation Exposure.

Microsoft makes a similar measurement boundary explicit in Bing Webmaster Tools. Its AI Performance reporting shows how often specific pages are cited, but Microsoft states that this reflects citation activity, not page importance, ranking or placement.

Source: Microsoft Bing Webmaster Tools AI Performance

So if citation rate rises after an intervention, the correct observation is: citation rate increased under the defined measurement conditions.

That does not automatically mean the source became more authoritative, the AI “trusts” the company more, the page caused a particular answer, representation accuracy improved, recommendation probability increased or the model changed permanently.

The intervention should follow the diagnosed problem rather than an optimisation checklist. Clearer owned information may be justified when entity facts are inconsistent; stronger evidence may be needed when important claims are weakly supported; technical search work may be appropriate when relevant information is inaccessible or poorly indexed; and further observation may be the right first action when the apparent gap is unstable.

For Google specifically, the company says existing SEO fundamentals remain relevant to AI Overviews and AI Mode, with no special AI schema or markup required for eligibility. Meeting those requirements still does not guarantee that a page will be crawled, indexed or served.

Source: Google Search Central: AI features and your website

For the complete baseline → diagnosis → improvement → retest process, use Kojable's AEO Strategy.

For Google's generative Search features, current guidance says established SEO fundamentals remain relevant and no additional technical requirements are needed specifically for AI Overviews or AI Mode.

That does not make conventional organic position a universal proxy for AI representation. Preserve sound SEO foundations, but measure the AI outcome that actually matters to the buyer decision.

For the deeper operating relationship between SEO, AEO and GEO, use Kojable's AEO Strategy.

Kojable uses a recurring sequence:

Monitor → Diagnose → Improve → Verify

Monitor establishes the current answer environment.

Diagnose identifies the commercially meaningful representation or evidence gap.

Improve selects the justified intervention and explains what should change, where and how.

Verify retests comparable buyer questions and records what moved.

This is not a claim that Kojable or any optimisation process controls a third-party AI system. It is a method for turning observable answers into a repeatable improvement process without confusing movement with causal proof.

If your company rarely appears, establish whether there is a genuine visibility gap. If it appears but is described incorrectly, investigate answer alignment. If evidence, content, entity clarity or discoverability is weak, AEO/GEO-compatible work may be an appropriate part of the improvement plan. If technical search infrastructure is the problem, SEO may be the immediate priority. And if the evidence is unstable, gather more observations before changing anything.

A mature programme can use several of these lenses at different points. What matters is keeping measurement, diagnosis, intervention and verification separate enough that each decision is supported by the right evidence.

Kojable is an AI answer alignment platform for B2B companies. Its role is to help teams understand and improve how AI represents the company through a recurring Monitor → Diagnose → Improve → Verify process.

The distinction matters because more activity is not the objective.

Better-aligned answers are.

Run your free AI brand audit to establish the current baseline.