One Keyword Research Framework for Both Google and AI Search

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Most teams still approach keyword research for Google and AI or LLMs like it’s 2017 - pull a massive keyword list, sort by search volume, target a few low-difficulty terms, and start publishing content.

That strategy no longer works in AI-powered search.

Today, users search differently. They ask conversational questions inside tools like OpenAI ChatGPT, Perplexity, and Google Gemini - and many of these queries never appear clearly in traditional keyword research tools.

The good news? You don’t need separate strategies for SEO and AI search optimization. You need one modern keyword research framework built around topics, intent, and real user questions.

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The goal is simple: stop thinking only in keywords and start thinking in search journeys.

Effective keyword research should:

• Reflect how real users search and ask questions
• Uncover topics, pain points, and intent
• Support both Google rankings and AI-generated answers
• Connect naturally to high-performing content ideas

If your research ends with a giant spreadsheet nobody uses, you haven’t built a strategy - you’ve built a document people ignore.

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How Can I Conduct Keyword Research for AI-powered Search Experiences?

To conduct keyword research for AI-powered search experiences, focus on user intent, conversational queries, long-tail keywords, and semantic search optimization. Analyze AI Overviews, featured snippets, and People Also Ask sections to identify how users phrase questions naturally. Use AI SEO tools to discover related entities, trending queries, and contextual keyword opportunities that improve visibility in AI-driven search results.

The 3×3 framework: Intent, Question Shape, Depth

Here’s the simple model we’ll use.

For each keyword or topic, we’ll look at three things:

1. Intent – What is the person trying to achieve?
   
2. Question Shape – How would they actually phrase it?
   
3. Depth – How deep of an answer do they need?
   

Do this, and you end up with content ideas that:

• match searcher intent for Google
  
• match natural language questions for AI tools
  
• match the right level of detail so people don’t bounce or feel overwhelmed
  

Let’s break those down.

1. Intent: what are they really trying to do?

Intent is the “why” behind a search.

At a basic level, most queries fall into a few buckets:

• Learn: “what is…”, “how does…”, “why does…”
  
• Compare: “best…”, “vs…”, “alternatives to…”
  
• Do / Buy: “pricing”, “template”, “book a demo”, “near me”
  
  

When you look at a keyword, ask:

• What would this person be doing 5 minutes before searching this?
  
• What are they hoping to do 5 minutes after?
  

Example:
“SEO vs AEO”

• Before: maybe they heard both terms in a webinar or tweet.
  
• After: they want to decide if they should change their strategy, hire someone, or ignore AEO.
  

Intent here is: compare and decide on a direction, not just “get a definition.”

Once you know the intent, you can:

• Design content that matches that job.
  
• Avoid writing fluffy explainers where you should be giving clear recommendations.

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2. Question shape: how would they actually ask?

AI tools especially are driven by how people phrase their questions.

For each topic, write down a few natural variations that feel like real sentences, not just tool output.

Example topic: “answer engine optimization”

Possible question shapes:

• “What is answer engine optimization and how is it different from SEO?”
  
• “Do I really need AEO or is SEO enough?”
  
• “How do I optimize my content for AI answers?”
  
  

These may or may not show up as exact keywords with volume—but they reflect real conversation.

This helps you:

• Turn headings into natural questions.
  
• Write intros that sound like you’re responding to the reader.
  
• Seed FAQs with questions that AI tools will recognize as useful.
  
  

3. Depth: how deep of an answer do they really need?

Not every query deserves a 3,000‑word guide.
Not every query can be satisfied with a 2‑line FAQ.

For each topic, ask:

• Is this a quick question? (definition, simple how‑to)
  
• Is this a decision question? (needs pros/cons, context, comparison)
  
• Is this a strategic question? (needs frameworks, examples, nuance)
  

Examples:

• “What is AEO?” → quick question (can start with 2–3 sentences + optional deeper sections)
  
• “SEO vs AEO” → decision question (requires clear comparison and recommendations)
  
• “How should B2B SaaS think about search in an AI world?” → strategic question (needs frameworks, cases, and caveats)
  
  

Depth then informs:

• how long the main piece should be
  
• whether it should be one article or a series/cluster
  
• how many supporting assets you need (FAQs, checklists, templates)
  
  

Step-by-Step Framework for AI-powered Keyword Research [2026 Updated Strategy]

To conduct keyword research for AI-powered search experiences, focus on topics, search intent, conversational queries, and content formats instead of isolated keywords. A strong AI SEO strategy helps your content rank in traditional search results, AI Overviews, and answer engines simultaneously.

Step 1: Start With Topics, Not Just Keywords

Modern keyword research starts with topics your audience genuinely searches for. Instead of chasing individual keywords, focus on:

• Products or services you offer
• Customer pain points
• Desired outcomes and goals

Examples include:

• “SEO for B2B SaaS”
• “Answer engine optimization”
• “AI search traffic”
• “Content systems for search”

These become your primary content clusters.

You can still use tools like Ahrefs and Semrush to discover related keywords, estimate competition, and identify search trends. However, keyword tools should support your strategy — not control it.

Step 2: Understand Search Intent

AI-powered search engines prioritize intent more than exact-match keywords. For every topic, identify what users actually want.

Most searches fall into three categories:

• Learning something new
• Comparing solutions
• Taking action

For example, someone searching “SEO vs AEO” is trying to compare strategies and decide which approach fits their business. Someone searching “What is AEO?” simply wants a quick explanation.

Intent mapping helps create highly relevant content that aligns with AI-generated answers and improves ranking potential.

Step 3: Add Conversational and Long-tail Queries

AI search experiences rely heavily on natural-language searches. That’s why conversational keywords and long-tail queries matter more than ever.

For each topic, create variations such as:

• “What is AI-powered search?”
• “How does AI SEO work?”
• “How do I optimize for AI Overviews?”
• “Best SEO strategy for SaaS companies”

You can find these queries through:

• Google People Also Ask
• Customer conversations
• Community discussions
• AI chatbot suggestions

These question-based keywords naturally become headings, FAQs, and featured snippet opportunities within your content.

Step 4: Match the Right Content Format

Different search intents require different content formats. Some topics need quick answers, while others need in-depth guides.

For example, “What is AEO?” works best as a concise educational explainer with FAQs. Meanwhile, “SEO vs AEO” performs better as a detailed comparison guide covering definitions, differences, benefits, and use cases.

Choosing the right content depth improves readability, user engagement, and visibility across both search engines and AI-generated search experiences.

Remember:

Effective keyword research for AI-powered search is no longer about building massive keyword spreadsheets. It’s about understanding how users search, what questions they ask, and which content formats best solve their problems.

When you combine topical relevance, conversational keywords, and clear search intent, your content becomes more likely to rank in Google search, AI Overviews, and answer engines alike.

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How Intent-Based Keyword Ressearch Framework Helps You Rank in Google and AI Search?

The best part about this framework is that you are not creating separate strategies for SEO and AI search optimization. The same structure helps your content perform better in both traditional search engines and AI-powered answer engines.

A large-scale study of 11,500 real-world queries found that AI Overviews appeared for more than 51% of searches and often pulled information from sources different from traditional organic rankings.

How It Helps in Google Search

Google rewards content that satisfies user intent clearly and completely.

When your content is built around real questions, strong search intent, and the right content depth:

• Users stay on the page longer
• They explore more sections
• Bounce rates decrease
• Content matches long-tail searches more naturally

These are strong quality signals for search engines.

For example, when someone searches “How does AI SEO work?” and immediately finds:

• A direct answer
• Step-by-step guidance
• Related FAQs
• Clear headings

Google understands that the page genuinely solved the query.

That improves rankings over time.

How It Helps in AI-powered Search

AI search tools work differently from traditional search engines. Instead of ranking pages alone, they try to extract, summarize, and reuse the best answers.

That means the optimized content structure matters a lot.

When your content includes:

• Clear question-based headings
• Concise direct answers
• Logical formatting
• Conversational phrasing
• FAQ sections

AI tools can understand and surface your content more easily.

You are essentially making your content “AI-readable.”

Instead of forcing AI systems to interpret messy content, you guide them directly toward the answer.

For example:

• A heading like “How Long Does B2B SaaS SEO Take?” is easier for AI systems to recognize than vague headings.
• A short, direct answer below the heading increases your chances of appearing in AI-generated summaries.
• Supporting context below the answer helps both users and AI engines understand the topic deeply.

That creates two layers of value:

• Better Google rankings
• Better visibility inside AI-generated answers

Example: Keyword Research for Google and AI Ranking Blogs/Pages

Let’s say your target topic is “SEO for B2B SaaS.”

Instead of treating this as a single keyword, break it down through the framework.

First, identify the intent.

Most users searching this topic want to understand:

• How SEO works for SaaS businesses
• Whether SEO is worth investing in
• What makes SaaS SEO different
• How long results take

This tells you the content needs both educational and strategic depth.

Next, build question-based sections naturally around user thinking:

• “How should B2B SaaS companies approach SEO?”
• “Is SEO worth it for early-stage SaaS startups?”
• “What makes SaaS SEO different from traditional SEO?”
• “How long does SEO take to work for SaaS companies?”

Immediately, the article becomes more aligned with real search behavior.

Now choose the right format.

This topic deserves a long-form guide because users need:

• Frameworks
• Examples
• Timelines
• Strategy explanations
• Decision-making guidance

From there, you can structure sections around:

• SaaS buying journeys
• SEO strategy by growth stage
• Content playbooks
• SEO and AEO working together
• Realistic growth expectations

Then layer AI optimization naturally into the article.

Add:

• Clear definitions
• FAQ sections
• Direct answers under headings
• Conversational subtopics

Now one piece of content can:

• Rank on Google
• Appear in AI Overviews
• Get quoted in AI answers
• Support sales conversations
• Drive qualified traffic

That’s the real goal of modern SEO content.

You Don’t Need to Overcomplicate Every Keyword for AI and Google Optimization

Not every topic needs a massive content framework.

Some keywords are simple and obvious.

For example:

• Brand pricing pages
• Login pages
• Simple definitions
• Support content

In those cases, a quick intent check and a few conversational keyword variations are enough.

Save deeper framework planning for:

• High-value commercial topics
• Educational content
• Comparison pages
• Core industry problems
• Strategic pillar pages

That’s where the biggest SEO and AI search gains happen.

Turn Your Keyword Research Into a Living System

Most keyword research fails because teams treat it like a one-time task.

The best-performing SEO strategies evolve continuously.

As you publish content:

• Add new customer questions
• Track performance trends
• Update outdated sections
• Expand successful topics
• Refresh declining pages

Over time, your keyword framework becomes more than an SEO document.

It becomes:

• Your content roadmap
• Your search strategy
• Your audience research system
• Your sales enablement resource

This is where modern SEO becomes much more powerful than simply “ranking keywords.”

How We Use This Framework at Sagashi

At Sagashi, we use this framework as the foundation of both SEO and AEO strategy.

Intent helps us understand why a piece of content should exist.

Question-based structure keeps content aligned with real human searches.

Content depth ensures users get the right level of information without overwhelming them.

From there:

• SEO helps users discover the content
• AEO helps AI systems understand and surface it

That combination is what drives visibility in modern search.

If you already have a list of important topics, products, or services you want to rank for, this framework makes it much easier to turn them into content that performs across both Google and AI-powered search experiences.

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