The way users are looking for information has changed. Search engines still dominate web traffic, but a growing number of consumers now turn to AI-powered engines to research products, compare services and answer questions.

As a result of this shift, brands must think about becoming visible in those channels too. It is no longer enough to focus on ranking in search engine results pages, to be successful business must now also consider AI-generated responses, recommendations, and citations.

This means that optimizing content for AI search is fast becoming extension of SEO strategy. However, the challenge is that AI search works differently. Instead of interpreting keywords, the LLMs interpret prompts conversationally, summarize information directly for users, and often provide answers without requiring users to visit a website.

This evolution in user behavior, creates a new question for content teams and marketers: What tools can help identify prompts, topics, and gaps that matter in AI search environments?

Why Optimize Content for AI Search?

Search engine traffic still outweighs AI-driven discovery, but LLMs are quickly gaining momentum. Consumers are now becoming as comfortable asking conversational questions as they are entering keyword-based searches.

This behavioral change matters because AI platforms process information differently. Instead of returning a list of links, these platforms generate synthesized answers based on the content they can access, understand, and trust.

As a result, brands must now think beyond rankings. They must create content that responds to the type of prompts users enter.

Research from Spotlight’s analysis of the battle between LLMs and search engines highlights how consumer attention is increasingly being split between search engines and AI assistants. Users are starting to expect direct answers, nuanced recommendations, and conversational interactions.

This means content strategy must change. Brands need to produce content that answers highly specific question and demonstrate strong contextual relevance. But, it must also aligh with the prompts user enter in AI tools.

In practice, this means content optimized for AI search often looks more comprehensive, more conversational, and more useful than content written purely for search engine rankings.

Is Optimizing Content for AI Search Different to Optimizing Content for Search Engines?

The short answer is that it is different, but both disciplines are closely connected.

SEO relies heavily on rankings, click-through rates, and driving users onto a website. However, AI search introduces a completely different dynamic because users can receive the information they need directly from the AI platform.

For example, when a user is enters the following prompt in an AI engine “What are the best CRM platforms for small businesses with remote teams?”, the user doesn’t have to visit a website, it is likely they will find the information they require in the AI-generated summary.

This means visibility extends beyond to blue links in search results. To exist, brands must now think about whether their content is being referenced, summarized, or cited within AI-generated answers.

In AI environments, user-behavior is more conversational and as a result, queries are getting longer, more detailed, and intent-rich. For example, instead of searching for “best hiking boots”, users might ask: “Which hiking boots are the most comfortable for a 2 week vacation in Austria”.

However, it must be stressed that optimizing for AI search and optimizing for search engines are not mutually exclusive. In many cases, foundations remain the same. In other words, the content must remain helpful, and authoritative, but also convey strong topic relevance. It must also fit in a clear site structure, demonstrate expertise, and present trustworthy information.

For those reasons, content that is often well-optimized for SEO often performs well in AI engines because both reward clarity, relevance, and authority. The main difference is that AI optimization requires deeper insight into conversational prompts and citation visibility. This is because AI engines interpret information semantically rather than purely through keywords.

What Tools To Use to Optimize Content for AI Search

As AI search evolves, marketers need tools to understand how their brand will appear across LLMs and conversational search experiences.

Query Fan-Out Tools

Query fan-out is the process AI systems use to expand a single user question into multiple related prompts and subtopics. This means that instead of interpreting a query literally, AI models explore associated concepts, comparisons, follow-up questions, and contextual variations.

This is a valuable tool for marketers because it reveals the wider network of prompts users indirectly associate with a topic.

Spotlight’s Fan-Out Queries feature helps brands identify these related conversational pathways. This can uncover gaps in content coverage and highlight opportunities to create pages that better align with real AI-driven search behavior.

For example, a business targeting “solar panel installation” may discover related AI prompts around financing options, maintenance requirements, energy savings, or installation timelines.

Rather than focusing on isolated keywords, fan-out analysis helps marketers build broader topical authority.

Prompt Volume Data

Understanding what users are asking AI systems is another challenge marketers face when writing content. Keyword tools are designed for search engines which means they cannot provide the correct information when a piece of content must be optimized for AI platforms.

Spotlight’s Prompt Volumes feature helps identify relevant prompts for brands to target. Therefore, allowing marketers to prioritize content opportunities based on conversational demand.

Instead of optimizing solely around keywords, brands can begin writing content around the questions and prompts users naturally ask AI assistants. This can improve visibility across AI-generated recommendations and increase the likelihood of being referenced in generated responses.

Preparing Your Content Strategy for AI Search

AI search is still evolving, but the direction of travel is clear. Consumers are increasingly comfortable using conversational AI tools to research products, evaluate services, and answer complex questions.

For marketers and content teams, this creates both challenges and opportunities.

The brands most likely to succeed will be those that understand how users interact with AI systems, create genuinely helpful content, and use specialized tools to uncover prompt opportunities, topical gaps, and citation visibility.

SEO remains essential, but AI optimization is rapidly becoming an important addition to modern content strategy.

What are the best tools for writing content optimized for AI search?