1. The Context: Clicks Decline, Answers Persist

Multiple independent analyses show substantial shifts in user behavior and traffic distribution:

AI Overviews reduce click-through rates to top organic results by 30–35%, with some categories reporting 40–80% declines on affected queries.

Data from Similarweb indicates news-related Google traffic dropped from roughly 2.3 billion to under 1.7 billion visits year-over-year as zero-click searches increased from 56% to 69% with AI summaries.

A Semrush study of 10 million keywords shows widespread adoption of AI Overviews, heavily concentrated in informational queries where answers are compressible.

Concurrently, AI sector spending is projected to expand at 30%+ CAGR, with total investment reaching the trillions by the early 2030s.

The implication is straightforward:

Traditional SEO aims for documents that attract clicks.

AI SEO aims for facts, entities, and structured evidence that can be selected and integrated directly into an AI-generated answer.

The remainder of this article covers twelve tactics that exist specifically within this AI-native environment.

2. Prompt Graph Coverage

Generative engines decompose a query into a graph of sub-tasks and reassemble the final answer using multi-step reasoning.

Implications for optimization

A complex query (e.g., “best project management tools”) is segmented into micro-prompts such as:

evaluation criteria

category comparisons

pricing structures

implementation timelines

AEO/GEO tactic

Design content mapped to predictable sub-tasks.

Ensure each section is self-contained and recoverable as a standalone answer block.

Title and structure micro-sections to match those sub-tasks.

Traditional SEO clusters long-tail keywords; AEO/GEO structures content around the model’s internal reasoning graph.

3. LLM Seeding

Unlike search engines, LLMs integrate knowledge directly into internal representations.

Observed behavior

Analyses consistently show generative engines favor:

community documentation

public glossaries

government or standards sources

neutral, non-commercial references

AEO/GEO tactic

Publish definitions and canonical explanations in public, neutral environments.

Contribute to open documentation, Q&A repositories, and standards-oriented surfaces.

Ensure key concepts appear where models acquire foundational knowledge—not only on brand-owned pages.

The objective is not to rank a URL, but to influence where the model learns authoritative facts.

4. Passage-Level Retrieval Optimization

LLMs retrieve passage-level units, not full pages.

Empirical findings

Citations in AI answers generally reference:

a single structured paragraph

a tightly scoped definition or comparison

a standalone table or evidence block

AEO/GEO tactic

Treat every H2/H3 section as an extractable reference.

Include the full claim, qualifier, and supporting data within the same passage.

Avoid requiring scroll-dependent context.

The goal is to create the clearest retrieval-ready paragraph available online for each micro-question.

5. Citation-Ready Evidence Packaging

Generative engines prefer structured, verifiable information that can support factual grounding.

Positive citation signals

semantic HTML

clearly labeled sections

tables, timelines, and quantified comparisons

explicit sources

AEO/GEO tactic

Provide numerical ranges, definitions, and classifications in machine-friendly formats.

Pair claims with clear evidence.

Build “proof blocks” that can be lifted directly into an AI answer.

Accuracy alone is insufficient; structure determines reusability.

6. Neutrality Engineering

Generative systems deprioritize text that resembles promotional copy or subjective claims.

Observed tendencies

AI engines disproportionately weight neutral, descriptive content.

Google has broadened spam criteria to include shallow or non-substantive material.

Over-optimized sales language correlates with reduced retrieval visibility.

AEO/GEO tactic

Keep evidence-oriented passages strictly factual.

Place any subjective or promotional framing in sections not intended for citation.

Maintain a clear separation between informational content and opinion.

Neutrality increases the likelihood of inclusion in the answer-generation stage.

7. Brand–Entity Memory Alignment

Models rely on entity consistency across the public corpus.

Observed issues

Different engines often describe the same brand inconsistently, especially when external profiles conflict or are incomplete.

AEO/GEO tactic

Define canonical facts: function, scope, audience, location, key attributes.

Ensure consistency across major third-party profiles (directories, data platforms, media bios).

Resolve outdated or contradictory public descriptions.

This strengthens the model’s internal representation of the entity, improving citation precision.

8. Competitor Co-Occurrence Structuring

Comparative prompts drive significant decision-making behavior in AI search.

Observed pattern

Brands frequently referenced in “vs.” or “best for” queries share common traits:

balanced third-party comparisons

consistent inclusion in category roundups

neutral, evidence-based descriptions

AEO/GEO tactic

Publish objective comparisons involving your entity and competitors.

Encourage third-party analysts and reviewers to include your brand in category discussions.

Prioritize transparency over positioning.

Rather than ranking for competitor terms, AEO/GEO focuses on establishing default peer set presence.

9. Source Blending Strategy

AI answers integrate content from multiple domain types—not only brand websites.

Documented blend

community Q&A

academic publications

documentation

standards and regulatory sites

neutral reviews

topical blogs

AEO/GEO tactic

Treat your digital footprint as an ecosystem.

Identify the non-Google surfaces influential in your domain and contribute accurate, consistent material.

Maintain identical core facts across environments to reduce ambiguity.

Generative retrieval is shaped by corpus composition, not by a single index.

10. LLM-Friendly Specification Publishing

Generative systems perform strongly when provided with clear rules, definitions, and structured processes.

High-performing formats

stepwise procedures

criteria lists

parameterized definitions

frameworks and decision trees

AEO/GEO tactic

Convert key knowledge into explicit specifications.

Document methodologies with clear boundaries and edge cases.

Provide precise definitions rather than broad positioning.

This offers models a reusable schema, increasing visibility in answer construction.

11. Training-Surface Expansion

Optimization increasingly includes surfaces adjacent to training data and retrieval corpora.

Examples of training-adjacent surfaces

public datasets

open PDFs

academic or industry research summaries

GitHub repositories

community documentation

AEO/GEO tactic

Publish high-signal, non-promotional material in formats conducive to ingestion.

Use permissive licensing where appropriate.

Consider every public artifact a potential retrieval point.

The objective is not indiscriminate exposure, but strategic selection of where foundational information lives.

12. Anti-Hallucination Engineering

Hallucinations arise when coverage is incomplete or ambiguous.

Research findings

Even advanced models produce fabricated details when factual grounding is weak.

AEO/GEO tactic

Publish concise fact sheets detailing key attributes, pricing structures, and policies.

Monitor how engines currently describe your brand.

Address inconsistencies through clear, repeatable information across third-party surfaces.

The aim is to ensure models converge on a small set of consistent descriptions, reducing the probability of errors.

13. Mention vs. Citation Optimization

In AI-generated answers, visibility has multiple states:

Not mentioned

Mentioned without citation

Mentioned and cited as evidence

Empirical insight

Citation likelihood correlates with:

structured formats

clarity of purpose

reliable metadata

corroboration from third-party sources

AEO/GEO tactic

Produce pages optimized both for narrative inclusion and evidence extraction.

Expand earned media to ensure neutral third-party sources can serve as citation anchors.

Measure mention vs. citation across engines and adjust accordingly.

This replaces the traditional “impression vs. click” metric with a more relevant “mention vs. citation” model.

Conclusion: Operating in the Current AI Answer Environment

Key realities:

AI summaries contribute to substantial click declines, particularly for informational queries.

Platforms emphasize answer quality and user satisfaction while expanding AI-generated summaries.

Hallucinations remain a structural issue, mitigated only through stronger grounding.

What can be influenced is strategy:

Treat AEO/GEO as distinct from traditional SEO.

Design content for retrieval, grounding, and reuse within generative systems.

Optimize not only for ranking but for recoverability, neutrality, and factual clarity.

Traditional SEO remains relevant, but it no longer defines the entire visibility pipeline. AEO/GEO addresses the broader environment in which answers—not links—are the primary unit of value.