A 90-day SEO playbook for AI-driven search visibility

SEO now sits at an uncomfortable intersection at many organizations.

Leadership wants visibility in AI-driven search experiences. Product teams want clarity on which narratives, features, and use cases are being surfaced. Sales still depends on pipeline.

Meanwhile, traditional rankings, traffic, and conversions continue to matter. What has changed is the surface area of search.

Pages are now summarized, excerpted, and cited in environments where clicks are optional and attribution is selective. 

When a generative AI summary appears on the SERP, users click traditional result links only about 8% of the time.

As a result, SEO teams need a clearer playbook for earning visibility inside generative outputs, not just around them.

This 90-day action plan outlines how to achieve this in a phased, weekly execution, with practical adjustments tailored to the specific purpose of the website.

Phase 1: Foundation (Weeks 1-2)

Define your ‘AI search topics’

Keywords still matter. But AI systems organize information around entities, topics, and questions, not just query strings.

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The first step is to decide what you want AI tools to associate your brand with.

Action steps

• Identify 5-10 core topics you want to be known for.
• For each topic, map:
  • The questions users ask most often
  • The comparisons they evaluate
  • “Best,” “how,” and “why” queries that indicate decision-making intent

Example:

• Topic: AI SEO tools
• Mapped query types:
  • Core questions: What are the best AI SEO tools? How does AI improve SEO?
  • Comparisons: AI SEO tools vs traditional SEO tools.
  • Intent signals: Best AI SEO tools for content optimization.

Where this shifts by website type

• Content hubs (media brands, publishers, research orgs) should prioritize mapping educational breadth – covering a topic comprehensively so AI systems see the site as a reference source, not a transactional endpoint.
• Services/lead gen sites (agencies, consultants, local businesses) should map problem-solution queries prospects ask before converting, especially comparison and “how does this work?” questions.
• Product and ecommerce sites (DTC brands, marketplaces, subscription ecommerce, retailers) should map topics to use cases, alternatives, and comparisons – not just product names or category terms.
• Commercial, long-funnel sites (B2B SaaS, fintech, healthcare) should anchor topics to category leadership – the “what is,” “how it works,” and “why it matters” content buyers research long before demos.

If you can’t clearly articulate what you want AI systems to associate you with, neither can they.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Create AI-friendly content structure

Generative engines consistently surface content that is easy to extract, summarize, and reuse. 

In practice, that favors pages where answers are clearly framed, front-loaded, and supported by scannable structure.

 High-performing pages tend to follow a predictable pattern.

AI-friendly content structures include: 

• A short intro (2-3 lines) that establishes scope.
• A direct answer placed immediately after the header, written to stand alone if excerpted.
• Bulleted lists or numbered steps that break down the explanation.
• A concise FAQ section at the bottom that reinforces key queries.

This increases the likelihood your content is:

• Quoted in AI Overviews.
• Used in ChatGPT or Perplexity answers.
• Surfaced for voice and conversational search.

For ecommerce and services sites in particular, this is often where internal resistance shows up. Teams worry that answering questions too directly will reduce conversion opportunities. 

In AI-driven search, the opposite is usually true: pages that make answers easy to extract are more likely to be surfaced, cited, and revisited when users move from research to decision-making.

Dig deeper: Organizing content for AI search: A 3-level framework

Phase 2: Generative engine optimization (Weeks 3-6)

Optimize for AI answers (GEO/AEO)

In generative search, content that gets surfaced typically resolves the core question immediately, then provides context and depth. 

For many commercial teams, that requires rethinking how early pages prioritize explanation versus persuasion – a shift that’s increasingly necessary to earn visibility at all.

This is where GEO (generative engine optimization) and AEO (answer engine optimization) move from theory into page-level execution.

• Add a 1–2 sentence TL;DR under key H2s that can stand on its own if excerpted
• Use explicit, question-based headers:
  • “What is…”
  • “How does…”
  • “Why does…”
• Include clear, plain-language definitions before introducing nuance or positioning

Example:

What is generative engine optimization?

Generative engine optimization (GEO) helps content get selected as a source in AI-generated answers.

In practice, GEO is the process of structuring and optimizing content so AI tools like ChatGPT and Google AI Overviews can interpret, evaluate, and reference it when responding to user queries.

How does answer-first structure change by site type?

• Publishers benefit from definitional clarity because it increases citation frequency.
• Lead gen sites see stronger mid-funnel engagement when prospects get clear answers upfront.
• Product sites reduce friction by addressing comparison and “is it right for me?” questions early.
• B2B platforms establish category authority long before a buyer ever hits a pricing page.

Add structured data (high impact, often underused)

Structured data remains one of the clearest ways to signal meaning and credibility to AI-driven search systems. 

It helps generative engines quickly identify the source, scope, and authority behind a piece of content – especially when deciding what to cite.

At a minimum, most sites should implement:

• Article schema to clarify content type and topical focus.
• Organization schema to establish the publishing entity.
• Author or Person schema to surface expertise and accountability.

FAQ schema, where it reflects genuine question-and-answer content, can still reinforce structure and intent – but it should be used selectively, not as a default.

This matters differently by site type:

• Content hubs benefit when author and publication signals reinforce editorial credibility and reference value.
• Lead gen and services sites use schema to connect expertise to specific problem areas and queries.
• Product and ecommerce sites help AI systems distinguish between informational content and transactional pages.
• Commercial, long-funnel sites rely on schema to support trust signals alongside relevance in high-stakes categories.

Structured data doesn’t guarantee inclusion – but in generative search environments, its absence makes exclusion more likely.

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Phase 3: Authority and trust (Weeks 7-10)

Strengthen E-E-A-T signals

As generative systems decide which sources to reference, demonstrated experience increasingly outweighs polish alone. 

Pages that surface consistently tend to show clear evidence that the content comes from real people with real expertise. 

Meaning, signals associated with E-E-A-T – experience, expertise, authoritativeness, and trust – remain central to how generative systems decide which sources to reference.

Key signals to reinforce:

• Clear author bios that establish credentials, role, or subject-matter relevance.
• First-hand experience statements that indicate direct involvement (“We tested…”, “In our experience…”).
• Original visuals, screenshots, data, or case studies that can’t be inferred or synthesized

This is where generic, AI-generated content reliably falls short. 

Without visible signals of experience and accountability, AI systems struggle to distinguish authoritative sources from interchangeable ones.

How different site types should demonstrate experience and authority

• Media and research sites should reinforce editorial standards, sourcing, and author attribution to support citation trust.
• Agencies and consultants benefit from foregrounding lived client experience and specific outcomes, not abstract expertise.
• Ecommerce brands earn trust through real-world product usage, testing, and visual proof.
• High-ACV B2B companies stand out by showcasing practitioner insight and operational knowledge rather than marketing language alone.

If your content reads like it could belong to anyone, AI systems will treat it that way.

Dig deeper: User-first E-E-A-T: What actually drives SEO and GEO

Build ‘citation-worthy’ pages

Certain page types are more likely to be cited in AI-generated answers because they organize information in ways that are easy to extract, compare, and reference. 

These pages are designed to serve as reference material – resolving common questions clearly and completely, rather than advancing a particular perspective.

Formats that consistently perform well include:

• Ultimate guides that consolidate a topic into a single, authoritative resource.
• Comparison tables that make differences explicit and scannable.
• Statistics pages that centralize data points AI systems can reference.
• Glossaries that define terms clearly and consistently.

Pages with titles such as “AI SEO Statistics (2025)” or “Best AI SEO Tools Compared” are frequently surfaced because they signal completeness, recency, and reference value at a glance.

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For commercial sites, citation-worthy pages don’t replace conversion-focused assets. 

They support them by capturing early-stage, informational demand – and positioning the brand as a credible source long before a buyer enters the funnel.

Dig deeper: How generative engines define and rank trustworthy content

Phase 4: Multimodal SEO (Weeks 11-12)

Optimize beyond text

Generative systems increasingly synthesize signals across text, images, and video when assembling answers. 

Content that performs well in AI-driven search is often reinforced across formats, not confined to a single page or medium.

• Add descriptive, specific alt text that explains what an image shows and why it’s relevant.
• Create short-form videos paired with transcripts that mirror on-page explanations.
• Repurpose core content into formats AI systems can encounter and contextualize elsewhere:
  • YouTube videos.
  • LinkedIn carousels.
  • X threads.

How this supports different site goals

• Publishers extend the reach and reference value of core reporting and explainers.
• Services and B2B sites reinforce expertise by repeating the same answers across multiple surfaces.
• Ecommerce brands support discovery by contextualizing products beyond traditional listings and category pages.

Track AI visibility – not just traffic

As generative results absorb more of the discovery layer, traditional click-based metrics capture only part of search performance. 

AI visibility increasingly shows up in how often – and where – a brand’s content is referenced, summarized, or surfaced without a click.

With 88% of businesses worried about losing organic visibility in the world of AI-driven search, tracking these signals is essential for demonstrating continued influence and reach.

Signals worth monitoring include:

• Featured snippet ownership, which often feeds AI-generated summaries.
• Appearances within AI Overviews and similar answer experiences.
• Brand mentions inside AI tools during exploratory queries.
• Search Console impressions, even when clicks don’t follow.

For long sales cycles in particular, these signals act as early indicators of influence. 

AI citations and impressions often precede direct engagement, shaping consideration well before a buyer enters the funnel.

Dig deeper: LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

These tools support different parts of an SEO-for-AI workflow, from topic research and content structure to schema implementation and visibility tracking.

• Content and AI SEO 
  • Surfer, Clearscope, Frase
  • Used to identify gaps in topical coverage and evaluate whether content resolves questions clearly enough to be excerpted in AI-generated answers.
• Schema and structured data 
  • RankMath, Yoast, Schema App
  • Useful for implementing and maintaining schema that helps AI systems interpret content, authorship, and organizational credibility.
• Visibility and performance tracking 
  • Google Search Console, Ahrefs
  • Essential for monitoring impressions, query patterns, and how content surfaces in search – including cases where visibility doesn’t result in a click.
• AI research and validation 
  • ChatGPT, Perplexity, Gemini
  • Helpful for testing how topics are summarized, which sources are cited, and where your content appears (or doesn’t) in AI-driven responses.

The rule that matters most

AI systems tend to favor content that provides definitive answers to questions. 

If your content can’t answer a question clearly in 30 seconds, it’s unlikely to be selected for AI-generated answers.

What separates teams succeeding in this environment isn’t experimentation with new tactics, but consistency in execution. 

Pages built to be understandable, referenceable, and trustworthy are the ones generative systems return to.