AI search strategy · Analysis

Do you really need separate SEO, AEO, GEO and LLMO strategies?

The acronyms describe real changes in discovery — but they do not require four disconnected strategies. A clearer approach follows the outcome: can your information be found, cited, named, recommended and acted on?

Mihir HarchekarUpdated 24 September 2026 · 9 minute read · Reviewed against official platform guidance
WhoNamed editorial ownership
HowPrimary-source research
WhyClarify strategy decisions
EvidenceFive linked official sources
01 / The language problem

Why are there so many AI search acronyms?

Every major change in discovery creates new language. The terms are useful when they clarify an outcome — and confusing when they repackage the same work.

Search engines now generate answers. AI assistants retrieve information from the web. A source may influence an answer without receiving a click. A brand may be cited without being named, or named without its website being cited. Agents can go further and complete tasks.

02 / Define the terms

Four lenses on the same discovery system.

Each acronym is a useful lens. None of them is a separate system.

TermPrimary focusDesired outcomeMain limitation
SEODiscovery and eligibilityRanking, visibility and visitsRanking does not guarantee citation
AEODirectly answering questionsAnswer inclusionOften reduced to FAQ formatting
GEOGenerated response visibilityCitation, mention, recommendationMeasurement remains variable
LLMORepresentation in LLM experiencesRetrieval and brand representationThe definition is inconsistent

Use the label that helps your team communicate. Measure the outcome the label is meant to improve.

03 / Platform guidance

What Google’s 2026 guidance changes.

Google now explicitly recognises AEO and GEO as common terms for AI-search visibility work. From Google Search’s perspective, however, optimisation for generative features remains part of SEO, because those experiences use established ranking and quality systems.[1]

Do the fundamentals well: make valuable information accessible, trustworthy and useful. Do not build a parallel site for an imagined "AI reader".
Read Google’s official generative AI optimisation guide ↗

The guidance rejects several fashionable shortcuts: no special schema is required; Google does not use llms.txt; prescribed content chunking is unnecessary; and rewriting solely for AI systems is not the goal. Unique, current and expert-led content remains central.

04 / Replace the acronym stack

Build one outcome funnel.

Organise the work around what the system — and the buyer — must be able to do next.

  1. 01

    Retrieve

    Can the system find the relevant information?

    CrawlabilityIndexabilityInternal linkingSemantic relevance

    MeasureIndexed priority pages · grounding-query coverage · pages surfaced

  2. 02

    Cite

    Does the system use your source as visible support?

    Original evidenceClear claimsCurrent factsPrimary research

    MeasureTotal citations · cited pages · citation rate · citation share[2]

  3. 03

    Mention

    Does the answer name the brand or product accurately?

    Entity consistencyCategory associationThird-party coverageReviews

    MeasureBrand inclusion · mention share · attribute accuracy · sentiment

  4. 04

    Recommend

    Does the platform recommend the brand for a relevant use case?

    Use-case evidenceComparisonsCase studiesIndependent validation

    MeasureRecommendation rate · shortlist inclusion · persona fit · reasons given

  5. 05

    Act

    Can an AI agent complete the next step successfully?

    Semantic HTMLAccessible formsClear labelsStable interfaces

    MeasureTask completion · form success · data accuracy · agent-assisted conversion[3]

Bing’s AI Performance reporting reinforces why these stages should be separated: citation data shows visible source use, not rankings, authority, traffic or engagement.[2] Google’s Search Console generative AI reports similarly expose impressions, pages, countries, devices and dates as visibility data — not a complete business outcome.[4]

05 / Operating model

One strategy. Different platform controls.

The acronyms do not require four independent teams. They require coordinated ownership across the discovery funnel.

Search + technical

Own retrieval

Indexation, internal linking, feeds, structured data and technical eligibility.

Content + subject experts

Own the answer

Clear claims, original evidence, current facts and useful explanation.

Brand + PR

Own recognition

Consistent positioning, expert participation, reviews and independent coverage.

Product + CRO

Own the action

Accessible journeys, reliable forms, clear choices and successful task completion.

The transformation matters more than the terminology. Optimise the connected journey from retrieval to action.
06 / Research library

Citations and primary sources.

5 sources verified
  1. [1]Optimising for generative AI featuresGoogle Search Central · Official guidance on SEO, AEO and GEOOpen source ↗
  2. [2]AI Performance in Bing Webmaster ToolsMicrosoft Bing · Citations, cited pages, grounding queries and citation shareOpen source ↗
  3. [3]Build agent-friendly websitesweb.dev · Screenshots, HTML, accessibility trees and task-ready UXOpen source ↗
  4. [4]Generative AI performance reportsGoogle Search Central Blog · Search Console AI-feature reportingOpen source ↗
  5. [5]Creating helpful, reliable, people-first contentGoogle Search Central · E-E-A-T and the Who, How and Why frameworkOpen source ↗