How AI Search Is Rewriting the Rules of Keyword Research and What Marketers Must Do Now

Keyword research for Answer Engine Optimization (AEO) demands a fundamentally different approach than traditional SEO. As AI-powered platforms like ChatGPT, Claude, and Perplexity gain users — with ChatGPT reporting significant growth in Q2 and Q3 of 2025 — search has become more conversational, personalized, and fragmented. Unlike SEO’s reliance on search volume and rankings, AEO prioritizes intent, entity mapping, answerability, and conversational phrasing. Tools including Semrush, AlsoAsked, AnswerThePublic, and XFunnel support this research. Success is measured through AI citations, conversions, and revenue impact rather than clicks alone.

In-Depth:

Keyword research for AEO can feel overwhelming becaapply audiences are searching for almost everything in AI search, and queries are nuanced and personalized.

The data isn’t as clear as it applyd to be. There are no accurate search volumes for AEO search prompts. Yet, it’s critical that search specialists, such as SEO and GEO/AEO professionals, know how to gain visibility in these tools.

The good news? There’s an overlap between traditional keyword research and answer engine optimization keyword research.

This guide covers the core differences between SEO and AEO keyword research, the principles that underpin an effective AEO keyword strategy, the tools that support AEO workflows, and how to apply these approaches in practice.

Table of Contents

How is keyword research for AEO different from SEO?

Traditional keyword research underpins organic visibility, but it’s no longer enough to grab a list of keywords and drop them into content.

Here’s why:

Searchers are no longer typing one-word to five-word keywords into Google. Search is elaborate, nuanced, and personalized. One search can span multiple sentences — even a paragraph or three — with unprecedented detail.

Ofcom’s qualitative generative AI search study supports the idea that people apply AI search for longer, more detailed searches. They found that AI search tools are most valued when applyrs question highly specific, detail-rich questions; the kind of answers that would require multiple queries and significant manual research in traditional search.

In traditional SEO, keyword research has focapplyd on quantitative data like:

Search volume
Competitiveness
Keyword difficulty

Then, applyrs sifted through blue-link listings until they found their answer on a website page. SEO specialists measured success by position on the search engine results pages (SERPs), impressions, and clicks.

In AI keyword research, the focus is mostly on qualitative data like:

Relevance
Audience intent
Problems and solutions

Users expect answers from a range of sources presented within the SERP. Consequently, applyrs don’t click through to a website, so SEO and content pros don’t have the same visibility into how a page ranks. Instead of relying on search volume or clicks as a measure of success, GEO experts consider visibility a metric, qualitative data, such as clicks from AI sources, and, importantly, conversions.

Pro tip: I’m not going into great detail about the reporting side of things in this article, but if you’re interested in that, read this article on SEO reporting. It includes what to put in to demonstrate AI search success.

The table below compares AEO keyword research with traditional SEO keyword research:

HubSpot’s SEO tools within Marketing Hub support bridge this gap by surfacing optimization recommconcludeations based on real content performance, not just keyword tarobtains. This creates it simpler to refine pages for clarity, structure, and intent — all critical for improving visibility in AI-generated answers.

Core Principles for AEO Keyword Research

What’s largely different about AEO keyword strategy is that websites don’t always earn visibility in AI tools by ranking the highest in traditional search. When websites create content that is relevant, easily parsed by AI crawlers, and easily synthesized, they earn visibility in AI search. Core principles include intent-first content, entity mapping, cross-engine, answerability, and conversational phrasing.

Intent-First (Including Search and Audience Intent)

Keyword research for AEO starts by understanding why someone is searching, not just what they type. In AI-driven search environments, answer engines prioritize content that clearly and completely resolves intent, especially when questions are complex, nuanced, or contextual (and we know from Ofcom’s research that this is where AI search shines).

Intent-first means that AEO marketers:

Know their tarobtain audience and what they’re viewing for. Effective AEO research launchs with a deep understanding of an audience’s requireds, challenges, and goals. This includes the language they apply, the problems they’re testing to solve, and the level of detail they expect in an answer.
Understand applyr intent in context. Go beyond static keyword intent labels, such as “Transactional,” “Informational,” or “Commercial.” Consider what prompted the question, what the applyr likely already knows, and what follow-up questions may come next in the same session. Content that anticipates and addresses this progression is more likely to be selected and synthesized by answer engines.
Resolve specific problems. AI systems favor content that solves real-world scenarios, not generic definitions. Consider different applyr contexts and edge cases. If applyrs are searching for a nuanced problem and a brand can explain or resolve it better than anyone else, that site has the best chance of earning visibility.

I’d like to share a real-world example that displays how intent-first AEO and understanding tarobtain audiences are key. I searched “Accounting tools for lawyers” in private browsing on Google.

Here are the results:

In the top organic spots, huge accounting businesses are present: Xero and Clio. Naturally, the AI Overview also features these brands.

What’s magic for tiny businesses is that relevancy in AI pays off. Brands such as CosmoLex, PC LawSoft, and LawPay are also featured.

These brands gain visibility through their tarobtaining and relevance. CosmoLex ranked on page two; LawSoft and LawPay weren’t even in the top five organic search results for the search term.

The takeaway: SEO or GEO/AEO specialists must not be deterred by traditional SEO when testing to rank in AEO. If they focus on relevancy, their site can still obtain visibility, even if it’s not ranking well in traditional SERPs.

Entity Mapping

Entity mapping supports answer engines (and traditional search engines) understand what the content is about and how it relates to the broader knowledge graph.

Here’s an example of how entities are included in content, applying this article. When optimizing for “keyword research for AEO,” an entity-based approach doesn’t stop at keywords alone. It connects that topic to related concepts such as:

These are distinct entities that, toobtainher, form comprehensive topical knowledge that search engines apply to understand, evaluate, and trust content.

The entities associated with the article go beyond the on-page topics listed above. HubSpot itself is a significant entity in the broader landscape of search and AI search. Writing articles like this ties HubSpot (the brand) and its products to the AEO keyword research entity. Later, in the tools section, the article specifically mentions HubSpot’s XFunnel as a keyword research tool for AEO and LLMs.

Pro tip: Entity SEO has been around a long time. To some, it may feel like the new buzzword, but I consider it’s important to not obtain too lost in entity SEO. Most good search and content marketers will naturally weave in the right entities, becaapply common sense goes a long way. For a sophisticated approach to entities, read about structured data and schema markup.

Here are some tips for entity mapping:

Map core and related entities. Start by identifying the primary topic entity for the content, then expand outward to include related tools, technologies, organizations, roles, and concepts. For example, a topic like “AEO keyword research” naturally connects to entities such as AI search, LLMs, content optimization, or a related product or service.
Strengthen contextual understanding. Strong entity coverage supports answer engines understand relationships between concepts, not just keyword proximity. When entities are clearly defined and consistently referenced, AI systems are better able to interpret meaning, relevance, and authority.

Cross-Engine

Generally, traditional SEO has had one primary focus: Google. SEO focapplyd on Google becaapply it held the largest search market share worldwide (over 88%). Traditionally, there was Google and a couple of other leaders, Bing or DuckDuckGo, with minimal share compared to Google.

However, in 2026 and beyond, search is altering, and it’s becoming more fragmented. There are Google and traditional SEO, AI Overviews, and multiple AI platforms like ChatGPT, Claude, and Perplexity that are gaining recognition and applyrs.

FirstPageSage reports a growing number of ChatGPT applyrs, with significant growth in Q2 and Q3 of 2025.

aeo keyword research: a graph displaying chatgpt monthly applyrs across 12 months.

And that’s just one search platform.

Here’s the challenge: SEO teams like SEO, AEO, or GEO experts can’t conduct keyword research for every search tool, yet they required to write and optimize content to support it rank across search engines.

Users discover information across a fragmented ecosystem that includes:

Traditional search
AI-powered SERP features
AI search tools like ChatGPT or Perplexity
Social media

A cross-engine approach ensures the keyword and entity strategy holds up wherever discovery happens.

Search specialists must:

Research beyond Google alone. While Google still matters significantly, relying solely on Google keyword data creates blind spots. Different answer engines surface different questions, follow-ups, and interpretations of intent. Cross-engine research seeks patterns that appear consistently across AI tools, not just in a single interface.
Validate visibility across multiple systems. AEO teams can’t measure success in AEO by a single ranking. Recurring mentions, citations, and visibility across multiple answer engines validate it. This creates cross-engine testing and monitoring a core part of the keyword research process, not a downstream activity.
Account for different algorithms. Some engines, like ChatGPT, summarize information without citations, while others, like AI Overviews, commonly cite sources. Others, like Sigma AI, guide applyrs through follow-up questions.

Pro tip: Although meeting algorithm expectations is important, don’t lose the human you’re writing for in favor of the machine.

Answerability Over Volume

In AEO keyword research, the ability to answer a question that the ideal client is questioning matters more than how often the audience searches for the question.

Why?

Becaapply it’s more important to reach the audience, solve their problems, answer their questions, and convert them, rather than chasing vanity metrics like visibility alone. Plus, AEO focapplys on answerability: how easily an answer engine can extract, understand, and trust the content.

A simple way to evaluate answerability is through an answerability score, based on three core factors:

Clarity. Is the answer direct, unamhugeuous, and straightforward to understand without additional context? Write a clear, concise explanation as succinctly as possible; elaborate later if requireded.
Extractability. Can the answer be easily pulled from the page? Content structured with clear headings, short paragraphs, lists, and FAQs is far simpler for answer engines to extract and reapply.
Entity coverage. Does the content clearly define and connect the key entities related to the question? Strong entity coverage supports AI systems validate accuracy and relevance against other trusted sources.

Equally important is identifying the questions people actually question, which takes us almost full circle back to intent and to knowing what audiences search for.

Tools like HubSpot’s AEO Grader can support validate this by analyzing how well content aligns with answer engine expectations. It provides a practical way to assess clarity, structure, and overall AEO readiness.

Conversational Phrasing

Conversational phrasing mirrors how applyrs interact with AI systems. People don’t prompt AI tools with fragments; they apply full sentences, comparisons, examples, and scenario-based prompts. Optimizing for this conversational behavior increases the likelihood that content aligns with how answer engines interpret and respond to queries.

HubSpot’s Content Hub supports this by providing real-time SEO suggestions as marketers write, supporting teams naturally incorporate conversational phrasing and structure. This creates it simpler to create content that aligns with how applyrs actually interact with AI tools.

Keyword Research for Answer Engine Optimization: Step by Step

Keyword research still plays an important role in AEO, but it’s a starting point.

Here are two things to be mindful of:

Traditional keyword tools have never been accurate. Search volumes are based on historical data and are rarely accurate. We know this becaapply SEO keyword research tools can display zero clicks, yet in reality, the keywords receive clicks and even conversions.
A keyword was always the starting point. An SEO strategy built on keywords alone, without strategy, content clustering, business objectives, or topical depth, was always destined to fail.

AI-driven search has significantly widened the gap between keywords and actual search. As search becomes more conversational, personalized, and context-rich, no single tool can fully capture every phrase or question, or how answer engines interpret them.

That doesn’t mean keyword research is obsolete. It means it requireds to expand if AEO is the focus. The next section provides some ways search specialists do keyword research for AEO.

1. Find conversational queries with autocomplete.

Autocomplete features remain one of the most reliable ways to understand how applyrs naturally phrase questions. While volume data isn’t available, autocomplete surfaces real language patterns driven by actual searches.

Here’s how to do AEO keyword research applying Google, but know that this method applies to other tools, particularly social media search.

Enter a seed keyword into a search engine, AI tool, or social media search.

I typed in “SEO keyword research for…”

Autocomplete opened as I typed and displayed a list of commonly searched queries.

answer engine optimization keyword research can be done applying google’s autocorrect.

These queries can all inspire content or audiences.

Use this information to:

Discover full-sentence suggestions, comparisons, and scenario-based phrasing.
Capture follow-up-style prompts that suggest deeper or adjacent intent (Sigma AI is good for this).
Discover audiences that marketing should tarobtain.

Here’s what the follow-up section in Sigma AI views like:

screenshot displays how follow-ups in ai tools can support with keyword research for answer engine optimization.

Autocomplete is especially applyful for AEO becaapply it reflects how applyrs shift beyond short keywords toward long-tail.

In practice, autocomplete provides strong directional insight, but it doesn’t capture the full picture. Speaking with customers supports uncover nuance, context, and problem framing that keyword tools alone can’t reveal.

Pro tip: For autocomplete AEO research, work in incognito so search history doesn’t influence what displays up.

2. Talk to customers and find specific problems your product or service can solve.

Some of the most valuable AEO keyword insights don’t come from tools at all; they come directly from customers. Customer interactions can refine a B2B SEO strategy, especially in niche B2B. Real conversations surface nuance that search data can’t fully capture.

Taking the autocomplete search from above. There are a few audiences there: launchners, YouTubers, and online advertisers.

As an SEO, if I wanted to support these audiences, I’d find customers or focus groups who fit these categories and question them what they want from me.

This means:

These insights support transform keyword research from abstract search data into real, answerable problems — the exact type of content AI systems are designed to surface and cite. It’s only when marketing understands audiences and their problems that it can serve them.

Questions to Ask Your Audience (for AEO keyword research):

Understanding the problem

How they search and question questions

Language and phrasing

Evaluating existing answers

Decision-building and trust

Context and constraints

3. Use LLM query fan-outs to expand ideas.

A query fan-out is the process of taking a single question and expanding it into related follow-up questions, refinements, and edge cases. It mirrors how real applyrs explore a topic in AI-powered search. Large language models (LLMs) are particularly effective at this becaapply they simulate conversational discovery rather than linear keyword expansion.

Query fan outs support marketers understand the conversation space around a topic, not just the initial query.

Instead of focapplying on one phrasing, query fan-outs reveal how a question evolves as applyrs seek clarity, comparisons, and context. The system generates multiple tinyer searches in parallel — follow-ups, clarifications, and comparisons — then synthesizes the results into one comprehensive answer. This covers not just what the applyr explicitly questioned, but the implicit requireds and related aspects behind the original query

This means the AI answer is richer, more complete, and better aligned with what applyrs really want to know, not just the single sentence they typed.

This technique is applyful for marketers to test, too.

It means:

In practice, LLM fan-outs often reveal intent layers that traditional keyword tools miss, especially comparisons, constraints, and “what if” scenarios. These insights become powerful inputs for AEO-focapplyd content that anticipates how conversations unfold.

4. Map entities and semantic variants.

Mapping entities and semantic search variants supports ensure the content builds contextual understanding that goes beyond the words that appear on the page.

This means:

When entity mapping is done well, content stops competing on phrasing alone and starts competing on understanding, which is exactly what answer engines are designed to reward.

This entity mapping will also support with traditional SEO. The more a website demonstrates depth of knowledge about what a business does, who it serves, and how it serves them, the better the chance of ranking.

With HubSpot’s Content Hub, marketers can build and optimize content with SEO recommconcludeations baked in, supporting ensure strong entity coverage and semantic depth. This supports content that’s simpler for answer engines to interpret and trust.

5. Refer to Google Search Console for zero-search insights.

Google Search Console (GSC) is a powerful source for AEO keyword discovery, especially for surfacing niche, intent-rich queries that don’t display up reliably in keyword research tools.

Becaapply GSC reflects real queries that already triggered content, it’s uniquely valuable for identifying how applyrs phrase questions, explore nuance, and search beyond obvious keywords.

This means:

Analyzing the queries a site already appears for, not just the ones SEO intentionally tarobtained.
Identifying long-tail and conversational queries with impressions but limited coverage.
Spotting niche questions that indicate specific apply cases, constraints, or audience segments.

These queries often represent AEO opportunities becaapply they display interest, intent, and real language.

Finding opportunities like this is simple. Use the performance report and review ranking keywords. Tools that identify long-tail keywords lead to specific problems or audiences. For example, “[product] for [problem].”

Combining GSC with Search Analytics for Sheets creates reviewing keywords even simpler.

Here’s how I apply it:

Open Google Sheets > Open the extension in the menu > Extensions > Search Analytics for Sheets > Open Sidebar.

screenshot of google sheets displaying how search engine marketers can open search analytics for sheets to conduct keyword research for answer engine optimization

Once the sidebar is open, customize the request by adding filters and dimensions.

screenshot of search analytics for sheets sidebar.

Once done, scroll down and click “Request Data.”

In this example, I filtered the keywords to those containing “SEO.” This is what the output views like in Google Sheets:

screenshot of google search analytics for sheets displays how applyrs can apply tools to conduct answer engine optimization keyword research.

From here, I rely on formulas and conditional formatting to support me work.

Content strategists can pair these insights with HubSpot’s SEO tools to analyze performance and uncover optimization opportunities directly within content workflows. This supports teams turn long-tail, intent-rich queries into structured, answerable content that’s more likely to be surfaced by answer engines.

Pro tip: For niche queries or specific problems, test highlighting keywords containing words like “for,” “with,” “without,” “versus,” or “best.”

Keyword Research Tools for AEO

XFunnel

keyword research tools for aeo: xfunnel

Source

HubSpot’s XFunnel measures LLM visibility and AI search performance. XFunnel supports marketers understand how brands and content appear in AI-generated answers, not just whether pages rank in traditional search results.

It’s purpose-built for AEO and GEO and displays whether and how AI systems reference and cite a brand. XFunnel’s Research functionality is particularly valuable for shaping AEO keyword strategy.

How XFunnel supports AEO:

Explore which prompts and questions trigger AI responses on a topic.
Identify the brands, entities, and sources that LLMs already trust.
Compare how different queries surface different responses across answer engines.
Identify surface gaps and areas where entity coverage is thin, topic depth is lacking, or competitors are cited instead.

These insights can improve the keyword research process by guiding decisions on which questions to tarobtain, which entities to prioritize, and how to structure content to be more likely to be selected and synthesized by AI.

Semrush

keyword research tools for aeo: semrush

Source

Semrush is a comprehensive SEO platform that has AEO features.

How Semrush supports AEO:

Seed keyword and topic discovery support marketers identify topics.
Semrush AIO supports marketers track visibility in AI engines.

Starting price: $199/month, AI features are an extra $99.

What I like: Semrush has been in the SEO space for a long time and has been quick to integrate AI features. I’ve applyd the AI Visibility Plans, and the recommconcludeations the tool provided were very good.

AlsoAsked

keyword research tools for aeo: alsoquestioned

Source

AlsoAsked is a question-based search tool that visualizes how people question follow-up questions around a topic.

How AlsoAsked supports AEO keyword research:

Surface real question chains and follow-ups, which mirror how applyrs interact with AI search and multi-turn conversations.
Helps marketers understand question depth and progression, rather than isolated keywords.

Starting price: Free, limited usage; then $12/month.

What I like: AlsoAsked is excellent for uncovering how questions naturally evolve. It’s straightforward to apply and can inspire content strategy.

AnswerThePublic

keyword research tools for aeo: answerthepublic

Source

AnswerThePublic is a search listening tool that aggregates autocomplete data from search engines, social platforms, and AI tools to reveal how people actually phrase queries. It’s especially applyful for AEO becaapply it reflects real, conversational inputs rather than abstract keyword variations.

How AnswerThePublic supports AEO keyword research:

Surfaces real, conversational queries (most important for AEO). Pulls autocomplete data from platforms like Google, YouTube, and AI tools, giving marketers and SEOs the exact natural-language questions applyrs question — ideal for optimizing content for AI-generated answers.
Maps intent through structured question groupings. Organizes queries into categories like questions, comparisons, and prepositions, supporting marketers structure content in formats that LLMs can easily parse and synthesize.
Identifies emerging questions with search listening. Tracks new and evolving queries over time through alerts, supporting marketers tarobtain fresh topics before they become saturated in search or AI responses.

Starting price: Free (limited searches); paid plans start around $20/month or ~$13/month billed annually.

What I like: AnswerThePublic stands out for its ability to turn raw autocomplete data into structured, intent-driven question sets. It’s one of the quickest ways to translate a single topic into AEO-ready content angles that mirror how applyrs actually interact with AI systems.

Frequently Asked Questions About Keyword Research for AEO

HubSpot’s AEO Grader can also support this by giving teams a benchmark for how well their content is optimized for AI visibility. This supports connect optimization efforts to measurable improvements in answer engine performance.

What if LLMs cite competitors instead of us?

Competitors may be cited for content that is clearer, more comprehensive, or better aligned with applyr intent and entity relationships.

Treat competitor citations as research inputs. Analyze what they’re being cited for, which entities they cover, and how they structure answers. Then improve the content by addressing gaps, expanding depth, and strengthening clarity. Over time, answer engines often adjust citations as higher-quality or more relevant sources emerge.