Artificial Intelligence has changed the way search engines work. Traditional keyword research focused on matching exact phrases with search queries. Today, search engines use AI to understand the meaning behind those queries and respond with answers instead of a list of links.
This article outlines how AI-driven search changes keyword research. It explains the key terms, how AI search engines interpret content, and what this means for search visibility.
Understanding AI-driven keyword research
AI Overviews (AIOs) are search features that use generative AI to answer questions directly in the search results. These overviews summarise information from multiple sources to provide conversational answers.
Key terms include:
AI search: A system that uses artificial intelligence to understand and respond to search queries.
Generative search: Technology that produces new answers based on the information it processes.
Conversational queries: Search inputs written in a natural, question-like format, similar to how people speak.
AI search engines use different techniques compared to traditional search engines. While traditional search engines rank content based on keyword matching and backlinks, AI systems use natural language processing to interpret context, user intent, and relationships between words.
This affects what appears in search results:
- AI-generated answers often appear in featured snippets or knowledge panels
- These answers can reduce the number of clicks to traditional web pages
Mapping keywords to user Intent in an AI World
AI search engines process queries by identifying the user’s intent. These systems group queries into four types:
Informational: Questions that seek facts or explanations
Navigational: Searches for a specific brand, site, or page
Transactional: Queries where the user is ready to act, such as making a purchase
Commercial: Searches that compare options, often before making a decision
Certain words signal specific intent types. For example, “how” and “what” suggest informational intent, “best” can indicate commercial investigation, while “buy” or “near me” often reflect transactional behaviour.
Question-based keyword research aligns with how users phrase queries in AI-driven environments. These queries often resemble spoken questions, like “What is the best luxury watch for investment?”
Search Performance Comparison
| Intent Type | Traditional Search Performance | AI Search Performance |
|---|---|---|
| Informational | High (snippets, articles) | Very High (direct answers) |
| Navigational | High (site links) | Moderate (may bypass sites) |
| Transactional | High (product pages) | Moderate (AI may summarise) |
| Commercial | Moderate | High (comparisons) |
Leveraging AI Tools for Advanced Keyword Discovery
- Choose specialised AI platforms
SEMrush, Ahrefs, and Moz are AI-supported keyword research tools. These platforms include features for measuring keyword difficulty based on AI-generated search results and tracking elements like AI answer boxes in search results.
These tools collect data on how keywords perform in both traditional and AI-powered search environments, helping identify how AI may surface information differently.
- Analyse NLP-based insights
Natural Language Processing (NLP) tools help identify how different keywords relate to one another semantically. Tools such as Clearscope and SurferSEO use NLP to show associated concepts and thematic clusters.
Using this data, it is possible to organise keywords into topic clusters. Topic clusters group related content under a central theme, which can improve semantic relevance in AI search.
- Explore predictive or trending data
AI tools that analyse trends can identify keywords gaining attention before they peak in search volume. Google Trends and Exploding Topics offer data on rising interest over time. Social listening platforms track what users are discussing across forums, social media, and blogs.
Incorporating competitor gap analysis
- Identify keyword gaps
Keyword gaps are search terms that competitors appear for in AI-generated search results, but your site does not. Tools like SEMrush and Ahrefs provide “Content Gap” reports that show these missing keywords.
- Benchmark competitor content
Competitor content can be analysed to understand why it performs well in AI-driven search. Key areas to review include:
- Content structure and organisation
- Depth and detail of information
- Question-answering formats
- Use of structured data (schema markup)
- Prioritise Low-Competition Opportunities
Some keywords have weak or incomplete AI-generated answers, typically marked by vague summaries or outdated information. These keyword opportunities can be targeted by publishing precise, well-organised content that addresses the query clearly.
Embracing long-tail and conversational keywords
Long-tail keywords are longer, more specific phrases that often have lower search volume but higher conversion rates. In the age of AI search, these keywords are becoming more important as they often match how people ask questions conversationally.
Benefits of long-tail keywords:
Lower competition: Fewer sites target these specific phrases
Higher relevance: They match specific user needs more precisely
Better AI visibility: They often align with how AI systems process natural language
Google Search Console can show new or unexpected queries that people have used to find a website. These queries can point to early shifts in how people phrase their questions. Answer the Public provides questions that users have asked related to a topic.
When validating which long-tail keywords to target, look for:
- Consistent appearances in Search Console impressions
- Relevance to your content and audience
- Alignment with your brand positioning
Validating AI insights with real-world data
AI-driven keyword strategies rely on both technology and ongoing observation. To understand how well AI-suggested keywords perform, they must be tested in live environments.
The testing process involves:
- Adding AI-suggested keywords into content
- Monitoring performance in both AI-based results and traditional rankings
- Updating content based on observed performance
Key metrics to track include:
- Click-through rates from AI Overviews
- Featured snippet appearances
- Citations in AI-generated answers
Future-focused keyword strategy
AI search systems interpret more than just written text. They also analyse voice commands, images, and video content. This is called multimodal search. To support this, keyword strategies include phrases people speak aloud and metadata for visual content.
Semantic relationships describe how words and topics connect in meaning. Creating topic clusters helps group related content in a way that AI can interpret clearly. A topic cluster includes a main page (pillar) and several related pages that link to it.
AI-specific content formats help search engines extract information accurately:
- Structured data provides machine-readable context
- FAQs use question-and-answer formats that align with conversational queries
- Clear headings support AI in identifying relevant sections
Moving from insight to action
Effective keyword strategies in AI search environments include three core actions:
- Map keywords to user intent: Align search queries with the purpose behind them—whether users are looking for information, comparing options, or ready to act.
- Balance AI tools with human judgment: While AI provides data and suggestions, human oversight ensures these insights support brand positioning and goals.
- Adapt based on performance: Use real-world data to guide ongoing keyword targeting and content structure adjustments.
At Passion Digital, we’ve seen how brands can thrive in AI-driven search by focusing on quality content that answers specific questions while maintaining their premium positioning. Our data-driven approach helps identify the most valuable keywords for your specific audience and business goals.
FAQs about modern AI keyword research
How do I combine voice search optimisation with AI keyword research?
Use question-based keywords that reflect how people speak in everyday conversations and write answers clearly and directly.
How often should I revisit my AI-driven keyword strategy?
Review AI keyword strategies every three months and monitor for new query patterns and changes in how AI systems display content.
What metrics best indicate success in AI search results?
Key metrics include click-through rates from AI Overviews, featured snippet appearances and citations in AI-generated responses.
Bringing it all together: Smarter keyword research for smarter search
The evolution of search, driven by AI, demands a smarter, more adaptable approach to keyword research. By focusing on user intent, leveraging AI tools and embracing long-tail and conversational keywords, marketers can stay ahead of the curve. The future of SEO isn’t about chasing trends; it’s about understanding your audience better and serving them content that is not only informative but contextually relevant. At Passion Digital, we help brands imagine better by marrying performance and creativity, creating intelligent, imaginative strategies that deliver lasting impact.