How to Rank in AI Search Results: A Practical Guide

AI search results work differently from traditional search rankings. Instead of competing for positions on a results page, you are competing to be included in a synthesised answer. This guide covers seven practical strategies for getting your business recommended by AI assistants.

How AI Search Results Work

Before diving into strategies, it helps to understand the mechanics. AI search results are generated through one of two primary mechanisms -- or a combination of both.

Training-Based Recommendations

Models like ChatGPT and Claude have been trained on large corpora of web content. When asked for a recommendation, they draw on statistical patterns in that training data. If your brand appeared frequently and positively in the training corpus, you are more likely to be mentioned.

The limitation here is that training data has a cutoff date. Information published after that date does not exist for the model unless it has web access. This means your historical web presence matters enormously for training-based recommendations.

Retrieval-Based Recommendations

Platforms like Perplexity and Google AI Overviews use retrieval-augmented generation (RAG). They search the live web at query time, retrieve relevant pages, and synthesise an answer from those results. This is closer to traditional search in that your current SEO performance directly influences your visibility.

Most modern AI platforms use a hybrid approach -- drawing on training data for general knowledge while retrieving live results for current information. This means you need to optimise for both dimensions.

What Makes AI Models Recommend Brands

AI models do not have preferences. They make recommendations based on patterns in data. Understanding what patterns lead to recommendations is the key to being included.

Frequency and Consistency

Brands that are mentioned frequently and consistently across multiple authoritative sources are more likely to be recommended. This is not about volume alone -- being mentioned on hundreds of low-quality sites is less valuable than being featured in a dozen high-authority publications.

Positive Sentiment

AI models pick up on sentiment. If the majority of content mentioning your brand is positive -- favourable reviews, successful case studies, enthusiastic media coverage -- the model is more likely to recommend you. Conversely, a pattern of negative mentions can suppress your visibility.

Relevance and Specificity

AI models assess how closely a brand matches the specific query. A query about "best accounting software for UK freelancers" will favour brands that explicitly serve UK freelancers over brands that offer generic accounting software. The more specific your content is about your target audience, the more likely you are to match specific queries.

Recency

For RAG-based platforms, recent content carries more weight. An article published last month is more likely to be retrieved than one published three years ago, all else being equal.

Citation Quality

When AI models cite sources, they create a feedback loop. Brands whose content is frequently cited as a source build cumulative authority. This makes being a cited source -- not just a mentioned brand -- a valuable goal.

Seven Strategies for Getting Recommended

1. Create a Definitive Resource for Your Niche

Every industry has questions that people ask repeatedly. Create the definitive answer to those questions on your website. Not a 500-word blog post, but a comprehensive, well-structured guide that covers every aspect of the topic.

AI models favour content that is:

• Thorough. Cover the topic completely, addressing common follow-up questions.
• Well-structured. Use clear headings, subheadings, and bullet points. AI models parse structure to understand content hierarchy.
• Factually rich. Include statistics, data points, examples, and case studies. Vague generalisations are less likely to be cited.
• Regularly updated. Stale content loses its citation value over time. Set a reminder to review and update your key resources quarterly.

2. Earn Mentions on High-Authority Sites

AI models weigh the authority of sources when making recommendations. A mention in a respected industry publication carries more weight than a mention on a personal blog.

Pursue opportunities to be featured in:

• Industry publications and trade magazines. Contribute expert articles, participate in industry roundups, and share original research that journalists can reference.
• Reputable review platforms. Encourage satisfied customers to leave reviews on Trustpilot, G2, Capterra, and sector-specific review sites. AI models reference these platforms when evaluating brands.
• News outlets. Local and national media coverage builds brand authority in training data. Offer your expertise as a source for journalists covering your industry.
• Educational and government sites. Mentions on .edu or .gov domains carry particular weight due to their perceived authority.

3. Optimise for Conversational Queries

People do not talk to AI assistants the way they type into Google. They ask full questions in natural language. Your content needs to match this.

Research conversational queries by:

• Looking at the "People also ask" section in Google results for your key topics.
• Reviewing the questions customers ask your sales or support team.
• Using AI assistants yourself to see how people phrase queries in your industry.
• Checking forums, Reddit, and Quora for how people discuss your topic.

Then create content that directly answers these questions. Start with a clear, concise answer before expanding with detail. This "answer first" structure is particularly effective for AI citation.

4. Implement Comprehensive Structured Data

Schema.org markup helps AI models understand your content programmatically. While not all AI platforms explicitly use structured data, it improves your content's machine-readability and supports the RAG retrieval process.

Implement at minimum:

• Organisation schema on your homepage, with your brand name, description, logo, and social profiles.
• Product or Service schema on your product/service pages, with pricing, descriptions, and reviews.
• FAQ schema on pages with frequently asked questions.
• Article schema on blog posts and content pages.
• LocalBusiness schema if you serve a specific geographic area.
• Review schema where you display customer testimonials or ratings.

5. Build a Consistent Brand Entity

AI models recognise brands as entities -- discrete things with attributes and relationships. The more consistently you present your brand across the web, the stronger your entity becomes in the model's understanding.

Ensure consistency across:

• Your website. Clear, consistent messaging about what you do and who you serve.
• Google Business Profile. Complete and accurate, with regular updates.
• Social media profiles. Consistent branding, descriptions, and links.
• Business directories. Accurate NAP (name, address, phone) data across all listings.
• Wikipedia and Wikidata. If your brand is notable enough for a Wikipedia page, ensure it is accurate and well-maintained. Wikidata entries feed directly into knowledge graphs.

6. Leverage the Power of Citations

When an AI platform cites your content as a source, it signals to both the AI and its users that your brand is authoritative. To increase your citation rate:

• Publish original research. Data, surveys, and studies that others reference create natural citation opportunities.
• Create reference content. Glossaries, templates, calculators, and tools that people link to and AI models cite.
• Be the primary source. When you publish information, present it as the original source. AI models prefer citing primary sources over aggregators.

For a deeper look at how AI platforms handle citations and how to monitor them, see our AI search optimisation guide.

7. Monitor, Learn, and Adapt

AI search is not static. Model updates, competitor actions, and changes in user behaviour all affect your visibility. Establish a regular monitoring routine:

• Weekly checks. Run your key queries across major AI platforms and note any changes.
• Monthly analysis. Review trends in your visibility data. Are you gaining or losing ground? On which platforms?
• Quarterly strategy review. Based on your data, adjust your content, outreach, and optimisation priorities.

The Role of Citations in AI Search

Citations deserve special attention because they serve a dual role. First, they influence the AI model's confidence in its recommendation. When a model can cite a specific source for a claim, it is more likely to make that claim. Second, citations drive traffic. Users who see a cited source often click through for more detail.

Different platforms handle citations differently:

• Perplexity displays citations prominently with numbered references and source links.
• Google AI Overviews link to the organic results that informed the AI summary.
• ChatGPT may include links when web browsing is active but does not always cite sources.
• Claude provides source information when asked but does not typically link directly.

Optimising for citations means creating content that AI models want to reference: factual, detailed, well-structured, and authoritative.

Monitoring Your Progress

Tracking your AI search visibility manually is possible but time-intensive. For each query, you need to check multiple platforms, record the results, and compare over time. This is where dedicated monitoring tools prove their value.

RivalScope automates this process, running your key queries across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews on a regular schedule. It tracks your brand's mentions, analyses sentiment, monitors competitor visibility, and identifies the specific actions most likely to improve your position.

For more on what to look for in a monitoring tool, see our AI SEO tools guide.

A Note on Patience

Unlike paid advertising, AI search optimisation does not produce instant results. Changes to your content, authority signals, and brand presence take time to be indexed, retrieved, and -- in the case of training-based models -- incorporated into the next model update.

Expect to see the earliest results on RAG-based platforms like Perplexity and Google AI Overviews, where changes to your web presence can affect visibility within days or weeks. Training-based platforms like ChatGPT and Claude will reflect changes more slowly, typically on the timescale of model updates.

The key is to start early and be consistent. Every piece of authoritative content you publish, every high-quality mention you earn, and every structured data implementation you complete contributes to a cumulative advantage that competitors will find increasingly difficult to match.

---

See how your brand performs in AI search today. Start your free RivalScope trial and get a complete picture of your AI visibility across every major platform.