SEO and AI-powered search: what impact on keywords

For years, keyword research gave SEO a fairly linear structure: identify a query, understand its search volume, analyse intent and build a page capable of answering that need better than competing results.

That process is still useful. Search behaviour, however, is becoming more complex.

With AI Mode, AI Overviews and conversational assistants, people can formulate much longer requests, add constraints, explain their context and continue through follow-up questions.

Google found that in 2026 the average AI Mode query is around three times longer than a traditional Search query.

A search such as "best running shoes" can therefore become something far more specific: "Which running shoes are suitable for someone who runs three times a week on asphalt, weighs around 80 kg and wants a cushioned model under €150?".

The commercial intent may be similar. The amount of context changes significantly.

This is where an AI-SEO strategy needs to evolve.

Keywords are still useful: their role is changing

Keywords such as "running shoes shops London" or "Google Ads agency London"continue to matter because they help identify demand, language and the size of a market.

They now work increasingly as a starting point.

A single keyword rarely represents all the ways a user can express the same need in a conversational environment.

The keyword "men’s puffer jacket", for example, may sit behind very different questions.

Say, for example, which model works below freezing, which insulation is lighter, which jacket performs well in rain, how to choose the right size or which brands match a specific style.

A modern keyword strategy therefore needs to map problems, selection criteria, comparisons, constraints and follow-up questions as well.

Search volume remains a useful signal. Intent coverage becomes increasingly important.

AI Mode and query fan-out: one question can become many searches

The way Google AI Mode works makes this shift particularly clear.

Google uses a technique called query fan-out, where the system generates several related searches from the original request to gather information across different aspects of the topic.

‍Google Search Central explains how query fan-out generates concurrent related queries to retrieve additional relevant information.

If someone asks how to fix a lawn full of weeds, for example, the system may also explore herbicides, chemical-free approaches and ways to prevent weeds from returning.

For SEO, this changes the way relevance should be considered.

A page can be useful even when it does not contain the exact wording used by the user, provided it covers a relevant part of the broader problem.

The practical consequence is significant: chasing hundreds of almost identical long-tail variations offers increasingly limited value.

Greater value comes from content that covers a topic with enough depth and clarity.

How keyword research changes in the AI era

Keyword research designed for AI-powered search should start from the main query and expand across the decision journey.

SEO research should therefore identify at least four dimensions: core need, related questions, selection criteria and likely follow-ups.

This creates a genuine intent map instead of a simple keyword list.

It also reduces the temptation to create five nearly identical articles for five formulations of the same need, a habit SEO can probably afford to retire.

From keyword to page structure

This development directly affects how content is structured.

A page designed around a single query can become too narrow for conversational search.

A stronger structure starts from the main question and develops the areas a user may want to explore immediately afterwards.

Headings become particularly useful here.

Each section should address a recognisable step: definition, problem, comparison, criteria, examples, limitations, decision factors or next action.

Google’s official guidance for generative AI search recommends useful, reliable content organised in clear sections and headings.

This helps users navigate the page and gives search systems a clearer understanding of the information available.

One page for every query? Increasingly unnecessary

The growth of conversational queries could suggest an apparently logical strategy: create a separate page for every possible question.

Google explicitly warns against this approach.

Its systems can understand synonyms, broader meaning and relevance even when the page does not contain the exact wording used in the query.

Google also stated that sites do not need to capture every possible long-tail variation to appear in generative Search experiences.

It therefore makes more sense to group questions that share the same intent and build a page comprehensive enough to answer the most important variations.

This also improves SEO architecture.

Fewer overlapping pages can mean less cannibalisation, clearer internal linking and stronger concentration of topical authority.

AI SEO also means more specific content

Generative search makes another issue increasingly visible: generic content is easy to replace.

A definition rewritten by hundreds of websites provides limited distinctive value when an AI system can summarise the same information directly.

Elements that are harder to reproduce become more valuable: first-hand experience, proprietary data, real examples, comparisons, methodologies, informed perspectives and specific product or service information.

Google’s 2026 guidance places particular emphasis on unique, expert-led and “non-commodity” content that adds value beyond information already widely available online.

For an SEO for AI strategy, this means placing more emphasis on the information density and originality of individual pages.

Technical SEO still matters

Google’s AI search experiences continue to rely on the Search index.

A page therefore needs to be crawlable, indexable and technically accessible to be eligible for AI Mode and AI Overviews.

Architecture, internal linking, canonicalisation, JavaScript rendering, page experience and accessible content continue to play a practical role.

Google also clarifies that no special markup or AI-specific files such as llms.txt are required for visibility in its generative search features.

Structured data retains its usual SEO role, without becoming a separate requirement for AI Mode.

The priority remains a technically clear foundation that allows content to be found and interpreted correctly.

From keyword strategy to topic strategy

The most useful change for SEO teams is probably methodological.

Instead of planning around:

keyword → page

it increasingly makes sense to think in terms of:

topic → intents → questions → content → connections

A primary keyword can become the centre of a broader cluster.

The core page covers the main intent, while supporting content explores genuinely distinct problems, comparisons or use cases.

This approach supports both traditional SEO and visibility across generative environments because it creates a more coherent body of information around the same subject.

It also fits naturally into a broader SEO, AEO and GEO strategy, where rankings, answers and citations become different parts of the same organic visibility ecosystem.

How to update existing content

There is no need to start from scratch.

Many existing pages can be improved by looking at how search behaviour is changing.

The first step is to review the queries already generating visibility: which questions appear in Search Console, and which intents remain poorly covered?

Then consider likely follow-ups.

If a page explains “how much X costs”, does it also explain what affects the price, how alternatives compare, which additional costs may appear and when the solution becomes worthwhile?

Missing areas can become new headings, FAQs or deeper sections.

The objective is to improve the page’s useful completeness while keeping its central focus clear.

How to measure an SEO strategy for AI-powered search

Measurement also needs a slightly broader view.

Rankings, impressions, clicks and conversions remain fundamental.

Alongside them, teams can monitor growth in conversational queries, visibility across priority questions, citations in AI environments, branded search trends and the quality of traffic arriving from new discovery surfaces.

Google has also started rolling out dedicated Generative AI performance reporting in Search Console, providing separate visibility into impressions generated through features such as AI Overviews and AI Mode.

The goal is to understand whether the brand enters the journey when users formulate more specific, decision-oriented requests.

A SEO for AI, AEO and GEO strategy therefore needs to look at classic search visibility, semantic coverage and presence within generated answers together.

In summary: keywords become the beginning of the research

Keywords remain a fundamental foundation for understanding the market and organising an SEO strategy.

AI-powered search adds more natural language, more context, more constraints and more follow-up questions.

Working on AI for SEO in 2026 therefore requires a wider view: start from the keyword, understand the complete need and build pages capable of covering the questions that develop around that intent.

The keyword shows where the search begins.

Content quality determines how much of that search journey the brand can actually cover.