Keyword Research in 2026: Intent, AI Queries, and the Modern Approach

Keyword research is the process of identifying the queries your target audience uses to find businesses, products, or information like yours — and understanding what those queries reveal about user intent.

In 2026, keyword research has evolved significantly. Keyword volume as a standalone metric has declined in strategic value. Intent has become the primary lens. And the emergence of AI search — where users ask natural language questions rather than submitting keyword fragments — requires expanding keyword research to include conversational query patterns, entity-based searches, and the question formats that AI answer engines are most likely to answer.

The Four Types of Search Intent

Search intent — what the user actually wants when they submit a query — is the foundation of modern keyword strategy. Google's ranking systems evaluate whether a page satisfies the intent behind a query, not just whether it contains the keywords.

Informational intent — The user wants to learn something. "What is local SEO", "how does schema markup work", "why is my website not ranking". These queries want comprehensive, accurate, educational content.

Navigational intent — The user wants to find a specific website or page. "Unity SEO login", "Google Search Console". Optimising for competitor navigational queries is rarely productive.

Commercial investigation intent — The user is researching options before making a decision. "Best SEO agency Norwich", "SEO vs Google Ads", "how much does SEO cost UK". These queries want comparison content, pricing information, and trust signals.

Transactional intent — The user is ready to take action. "SEO agency Norwich contact", "hire SEO consultant", "book SEO audit". These queries want clear calls to action and frictionless conversion paths.

Keyword Research for AI Search

AI search introduces query patterns that traditional keyword research tools undercount. When users interact with ChatGPT, Perplexity, or Google AI Mode, they ask conversational questions — full sentences, natural language, multi-part queries — rather than the keyword fragments they type into traditional search.

Conversational query patterns to research:

• "What is the best SEO agency for a small business in Norwich?"
• "How much should I budget for SEO for my Norfolk restaurant?"
• "What's the difference between local SEO and regular SEO?"
• "Should I do SEO myself or hire an agency?"

These queries rarely appear in traditional keyword tools with significant volume — because users type fragments like "SEO agency Norwich" into Google but ask full questions to AI systems. Effective keyword research in 2026 identifies both.

How to research AI query patterns:

1. Use "People Also Ask" results in Google — these represent the question formats Google is already synthesising AI answers for
2. Test target topics in ChatGPT, Perplexity, and Google AI Mode to understand how users phrase queries to AI systems
3. Use Semrush's AI-generated content tracker to identify which queries in your space are triggering AI Overviews
4. Review your site's FAQ section — the questions real users ask are often the most valuable AI-optimisation targets

Building a Keyword Matrix

Effective keyword research produces a structured matrix, not a flat list. A keyword matrix organises target queries by:

• Intent category (informational, commercial, transactional)
• Topic cluster (which pillar page or cluster article this query belongs to)
• Query format (keyword fragment, question, conversational AI query)
• Volume and difficulty (Ahrefs KD, Semrush Keyword Difficulty)
• AI Overview trigger (does this query trigger an AI Overview? Are you eligible to be cited?)
• Priority (which queries should be targeted first based on commercial value and achievability)

This matrix drives content prioritisation, content structure decisions (question headings for high-AI-trigger queries), and schema decisions (FAQPage for question-format queries likely to trigger AI citation).