Tag: SEO

I’ve noticed that many people labeling things as “AI SEO” are just applying traditional SEO concepts dressed up with new buzzwords.

AI SEO, however, stands apart.

When I explore how AI tools like AI Overviews, ChatGPT, and Perplexity sort and condense information, it’s clear there are strategies available to us now that simply didn’t exist in the old Google 10-blue-links era.

In this article, I’ll walk you through those unique AI SEO tactics, leveraging concrete data, not just hopeful speculation.

Feeling the drop in clicks, right? Here are some compelling facts:

• Research has shown that when Google’s AI Overviews were applied, the click-through rates to top organic results fell by about 30 to 35%. In some cases, publishers reported losing 40 to 80% of their search traffic.
• According to an analysis with Similarweb data, news traffic from Google declined from around 2.3 billion to under 1.7 billion visits in just a year as zero-click searches increased from 56 to 69% after AI summaries were introduced.
• From a Semrush study on 10 million keywords, AI Overviews now frequently appear, especially for informational queries, changing the visibility landscape by consolidating multiple sources into a single AI-generated response.

Meanwhile, the AI market is expanding at a rate of over 30% CAGR, with projections suggesting that total AI spending will reach into the trillions by the early 2030s.

AI SEO is about optimizing not just for clicks but for factual representations that earn places within AI-generated answers.

Here are 12 exclusive tactics to thrive in this new landscape:

1. Prompt Graph Coverage

Traditional SEO treats a query as a single unit mapped to a page.

AI engines deconstruct queries into graphs of subtasks and address each. Google mentions “multi-step reasoning” for tackling complex queries at once. Academic research on AI SEO also indicates that AI functions break down queries into sub-questions, synthesizing information across sources.

AI SEO strategy: Model that graph personally.

• Transform the primary query into predictable sub-questions.
• Create detailed sections that fully address each subtask.
• Ensure each section is self-contained and suitable for the specific micro-intent.

When writing about “best project management software,” consider prompting for:

• “criteria for agencies”
• “comparison vs spreadsheets”
• “pricing breakdown by seat”
• “implementation timeline”

Each needs its own precise, well-titled segment.

2. LLM Seeding

While traditional search engines don’t absorb all content into their algorithms, LLMs do.

AI SEO shows a preference for neutral sources like Wikipedia and governmental documents over branded marketing pages, so contributing to factual and earned sources is key. Backlinko’s findings reinforce engaging in the right content surfaces for training and retrieval.

AI SEO-only move:

• Release definitions, glossaries, and FAQs publicly.
• Contribute to places where models learn their foundational facts.
• Sow Q&A style content in widely used forums.

This is about showing where the model will find the canonical truth, making sure it’s your content.

3. Passage-Level Retrieval Optimization

Traditional SEO generally ranks entire pages. AI engines retrieve information at a passage level.

Studies show that models cite specific highly structured passages, not entire pages.

AI SEO-only move:

• Treat each heading as a standalone answer.
• Include all claims, qualifiers, and evidence within one passage.
• Minimize the reader’s need to traverse the page for logic.

Stand out as the model’s go-to reference for any particular question.

4. Citation-Ready Evidence Packaging

AI engines must justify their responses.

Studies indicate pages commonly cited by AI engines have structured data, semantic HTML, and explicit evidence like tables. The absence of verifiable facts increases the tendency for models to hallucinate.

AI SEO-only move:

• Present data in machine-readable formats: tables, comparisons, glossaries, checklists.
• Support each strong claim with solid statistics and a source.
• Ensure the model can easily extract your “proof block.”

You need to be verifiable and structured for easy reuse.

5. Neutrality Engineering

Models favor neutral, non-promotional sources over overtly commercial ones.

According to research, Google’s definition of spam has widened to include content that lacks depth, especially in AI Overviews.

AI SEO-only move:

• Remove promotional language from pages aimed at being cited.
• Ground your narrative in facts, comparisons, and third-party validations.
• Create separate layers for opinion and positioning.

Continue to sell, but ensure your main content remains neutral and evidence-based.

6. Brand-Entity Memory Alignment

While search engines focus on page-query matching, LLMs concern themselves with how well your entity is understood across the board.

Studies suggest variance in how engines perceive brands, often favoring well-recognized and consistently presented entities.

AI SEO-only move:

• Clearly define your brand’s canonical facts: identity, operations, audience.
• Ensure consistency across high-authority platforms.
• Rectify outdated or conflicting information across channels.

Train the model to understand who you are, not just what metadata say.

7. Competitor Co-occurrence Hijacking

A significant portion of buying intent lies in comparative prompts.

AI engines synthesize answers by comparing multiple competitors. Research shows brands frequently appearing in comparative content often benefit in AI outputs.

AI SEO-only move:

• Position your brand in neutral, third-party comparison content.
• Craft balanced comparisons that consider multiple competitors honestly.
• Encourage inclusion in “shortlist” content likely used in category training.

Traditional SEO hopes for a ranking opportunity. AI SEO embeds you within the model’s default competitive landscape.

8. Source Blending Strategy

In AI search, a “SERP” is a blend of diverse sources, not just a page.

Semrush and others note that AI engines pull from a wide range of sources, favoring community and documentation in many sectors.

AI SEO-only move:

• Develop your presence into an ecosystem, beyond a single website.
• Identify which non-Google platforms in your niche influence LLMs and establish credibility there.
• Use consistent terminology to form a coherent online identity.

Your goal is corpus optimization, not just ranking in an index.

9. LLM-Friendly Specification Publishing

Models excel at snapping structures into place.

Content rich with detailed structures like definitions, lists, and stepwise instructions performs best in AI responses.

AI SEO-only move:

• Share your key frameworks as open specifications.
• Convert ambiguous messaging into clear decision-making instruments.
• Document methodologies in public, thorough formats.

Offer the model a blueprint beyond just marketing speak.

10. Training-Surface Expansion

AI SEO is emerging as an industry on its own, backed by significant future investments.

However, this investment is not focused on just one index.

AI SEO-only move:

• Explore potential training surfaces within your specialty like open datasets and public reports.
• Place your best insights there openly, ready for retrieval or training.
• Treat every public snippet as training material, not only lead generation.

You are determining where and how models will encounter your reality.

11. Anti-Hallucination Engineering

Hallucination in AI isn’t hypothetical.

Benchmarking and academic studies consistently show that AI can produce false details, particularly in low-coverage or vague topics.

AI SEO-only move:

• Distribute concise fact sheets about your entity across neutral sources.
• Remove contradictory public claims wherever possible.
• Monitor and adjust how AI systems portray your brand.

While eliminating hallucinations is impossible, you can ensure the model opts for a well-documented version of you.

12. Mention vs. Citation Optimization

In AI searches, there are three distinct states:

• Your brand is not mentioned.
• Your brand is mentioned, without citation.
• Your brand is both mentioned and cited.

Research indicates that citation patterns relate closely to specific quality signals on the page and sites.

AI SEO-only move:

• Design pages that meet both narrative and citation criteria.
• Grow earned media allowing third-party sites to be cited.
• Map your current state across engines and craft campaigns to elevate your position.

Just as traditional SEO distinguishes between impressions and clicks, AI SEO separates mentions from citations, and this is crucial for visibility.

The Uncomfortable Balance

We must face some key truths:

• AI summaries are raising zero-click behavior, compressing publisher traffic, with click-through rate declines between 15 to 80% depending on the query.
• Platforms claim higher quality clicks and satisfaction while expanding these features into search.
• Despite advances, LLMs still hallucinate, reducing errors involves better grounding and evaluation.

As individual brands, we cannot change these broad issues. But we can adapt to the current landscape:

• Treat AI answers not as a novelty added to SEO but as a unique channel.
• View AI SEO as a standalone channel with specific levers, measurements, and content styles.
• Create content for retrieval, trustworthiness, and reuse by generative systems.

Traditional SEO isn’t obsolete, but it is only part of the journey now.

Inspired by this post on Search Engine Land.