How To Create and Optimise Your Content for AI Search

This episode was produced in partnership with Profound.

Your customers are asking ChatGPT which project management software to buy. They’re getting product recommendations from Google’s AI Overviews. They’re turning to Perplexity for business decisions that used to require hours of research.

And unless your content appears in these AI responses, you simply don’t exist in these conversations.

ChatGPT now processes over one billion searches daily. Google’s AI Overviews are becoming the default search experience. Yet most businesses are still optimising content using strategies designed for how Google worked in 2019.

Your customers aren’t just searching differently — they’re making purchasing decisions based on AI recommendations. And if your content isn’t optimised for AI Search, you’re invisible in these conversations.

This isn’t about abandoning traditional SEO. It’s about expanding your content strategy to dominate the platforms where tomorrow’s customers are already searching today. The businesses that master AI Search Optimisation and create an AI Search strategy now will establish an insurmountable advantage while their competitors scramble to catch up.

Executive Summary

AI Search Optimisation requires a fundamentally different approach than traditional SEO. While SEO for Google focuses on ranking individual pages, AI Search success depends on creating content that gets cited and referenced across multiple AI platforms.

The key differentiators for AI Search success:

• Experience-driven content that demonstrates first-hand knowledge rather than generic information
• Answer-first structure that provides immediate value instead of burying recommendations
• Multi-angle topic coverage to capture the “query fan-out” approach AI tools use
• Platform-specific optimisation since different AI tools prefer different content types
• Citation-worthy formatting with clear headings, bullet points, and structured data

The businesses winning in AI search aren’t just creating more content — they’re creating fundamentally different content that AI tools want to cite and recommend.

Why AI Search Optimisation Matters for Your Business Now

The numbers tell a compelling story. ChatGPT’s one billion daily searches might seem modest compared to Google’s eight billion, but the trajectory is clear: AI Search is only moving in one direction.

More importantly, there are two critical reasons why AI Search visibility directly impacts your bottom line.

First, AI Search drives qualified traffic. When AI tools cite your content, users click through to explore further. Unlike traditional search, where users might bounce between multiple results, AI citations carry implicit endorsement value. If ChatGPT or Google’s AI Overview recommends your business, that recommendation carries significant weight with potential customers.

Second, and perhaps more crucially, AI Search gives you the opportunity to influence customer decisions at the point of discovery. When someone asks, “What’s the best project management software for creative agencies?” and your content gets referenced in the AI response, you’re not just getting traffic — you’re shaping the customer’s entire decision-making process.

This influence extends beyond direct recommendations. If your content explains why certain features matter in project management software, or highlights common implementation challenges, that context becomes part of the AI’s response. You’re essentially educating prospects through the AI tool itself.

The businesses that recognise this opportunity now — while AI Search is still in its relative infancy — will establish positioning that becomes increasingly difficult for competitors to challenge as these platforms mature.

What Content Formats AI Tools Actually Prefer

AI tools exhibit distinct preferences that differ markedly from traditional search algorithms. Understanding these preferences is crucial for optimising content for AI Search effectively.

The answer-first revolution represents perhaps the most significant shift. Historically, content creators buried their recommendations deep within articles to maximise page views and ad exposure. Today’s AI-optimised content flips this approach entirely.

Take the highest-performing articles about robo advisors in ChatGPT results. Every single one follows the same structure:

• Headline with year specification (“Best Robo Advisors 2025“)
• Immediate credibility indicators (author expertise, verification badges)
• The actual recommendation in the second paragraph
• Supporting comparison tables and methodology
• Generic educational content is relegated to the bottom

This structure succeeds because AI tools want to extract clear, quotable answers quickly. They don’t want to parse through lengthy introductions or hunt for buried conclusions.

Structured data elements that AI tools consistently favour include:

• Comparison tables that clearly contrast options
• Numbered and bulleted lists for easy extraction
• FAQ sections that address related queries
• Clear subheadings with descriptive titles
• Statistical data points that support recommendations

Content depth matters differently in AI search. Rather than creating one comprehensive 5,000-word article, successful AI optimisation often involves creating multiple focused pieces that cover a topic from various angles. This approach aligns with how AI tools break down complex queries into multiple parallel searches.

The most successful content combines immediate actionability with comprehensive coverage. Users get instant value, while AI tools find exactly what they need to cite in their responses.

Where AI Tools Find and Cite Your Content

The citation ecosystem for AI Search operates on fundamentally different principles than traditional search rankings. Understanding where AI tools look for content — and why they choose certain sources over others — is essential for strategic content placement.

Platform-specific preferences create distinct opportunities across different AI tools:

Google AI Overviews demonstrates a clear preference for “owned media” — content from the actual businesses being discussed. If you’re researching robo advisors, Google’s AI is more likely to cite and link to Vanguard’s own website when discussing Vanguard’s services. This creates significant opportunities for businesses to optimise content for AI search on their own properties.

ChatGPT, conversely, shows a strong bias toward “earned media” — third-party content that discusses and compares various options. It rarely links directly to business websites, instead preferring content from Forbes, NerdWallet, Business Insider, and similar third-party publishers that review and compare services.

The citation hierarchy follows predictable patterns:

• High-authority third-party publications (Forbes, Time, established industry sites)
• Specialised comparison and review sites
• Academic and research institutions
• User-generated content platforms (Reddit, specialist forums)
• Company-owned content (depending on the AI tool)

Geographic and topical authority also influences citation likelihood. AI tools appear to weight sources based on their established expertise in specific domains. A financial publication’s content about investment tools carries more weight than a general business site covering the same topic.

This creates strategic implications for content distribution. Success in AI search often requires a multi-platform approach rather than focusing solely on your own website. The businesses winning AI search citations are those that have established thought leadership across multiple relevant platforms.

Who Your Real Competitors Are in AI Search Results

Traditional SEO competition analysis becomes insufficient when optimising content for AI Search. Your real competitors aren’t necessarily the businesses you compete with directly — they’re the content creators who consistently get cited in AI responses for your target topics.

The third-party dominance in AI citations means you’re often competing against publishers rather than direct business competitors. When someone asks ChatGPT about the best marketing automation tools, you’re not primarily competing against other marketing automation companies. You’re competing against the Forbes articles, NerdWallet reviews, and industry comparison sites that AI tools prefer to cite.

This creates a fascinating competitive dynamic. The businesses that succeed in AI Search are often those that have invested in becoming the authoritative voice within third-party publications, rather than simply optimising their own websites.

Content format competition reveals additional layers of complexity. You might rank #1 on Google for “best project management software,” but if your content is a traditional long-form article while AI tools are citing concise comparison tables from industry publications, your Google success becomes irrelevant in AI Search.

The experience advantage provides a unique competitive moat. Generic AI-generated content about business software cannot compete with content that includes actual case studies, implementation experiences, and real-world usage scenarios. This is where businesses can establish unassailable competitive advantages in AI search.

Consider our approach at Elite Renewables. When creating content about heat pump installation for swimming pools, we don’t compete with generic HVAC advice articles. Instead, we document actual client projects, engineering challenges, and specific solutions we’ve implemented. This creates content that AI tools cannot replicate and competitors cannot easily match.

The authority transfer effect means that businesses cited in high-authority publications often continue to benefit from that authority across multiple AI tools and queries. A single well-placed mention in a respected industry publication can generate AI citations across dozens of related queries.

When to Prioritise AI Search Over Traditional SEO

The decision to prioritise AI Search Optimisation requires careful consideration of your audience, business model, and market dynamics. While both strategies can coexist, resource allocation decisions often demand prioritisation.

High-value, low-volume queries represent the sweet spot for AI Search prioritisation. Traditional SEO excels at capturing high-volume queries, but AI Search often provides superior results for specific, intent-heavy queries that generate qualified leads.

For example, someone asking “What’s the best enterprise project management solution for creative agencies with remote teams?” represents a highly qualified prospect. AI tools excel at handling these specific, multi-layered queries, making AI Search Optimisation particularly valuable for businesses serving niche markets or specialised use cases.

Decision-stage content benefits disproportionately from AI Search Optimisation. While traditional SEO captures users throughout the entire customer journey, AI Search queries often indicate higher purchase intent. Users asking AI tools for specific recommendations are typically closer to making decisions than those conducting general Google searches.

Technical and professional services see exceptional returns from AI search optimisation. Legal advice, financial services, healthcare, and professional consulting often involve complex queries that AI tools handle more effectively than traditional search. Users in these sectors increasingly turn to AI for nuanced questions that require contextual understanding.

The authority multiplier effect makes AI search particularly valuable for thought leadership positioning. Being consistently cited by AI tools establishes credibility that extends beyond the immediate search query. This positioning becomes self-reinforcing as industry recognition increases citation likelihood.

Resource efficiency considerations favour AI search in specific scenarios. Creating one piece of high-quality, experience-driven content that gets cited across multiple AI platforms often provides better ROI than creating multiple pieces optimised for different traditional search queries.

However, traditional SEO remains superior for broad market capture and building foundational online visibility. The optimal approach typically involves integrating both strategies rather than choosing exclusively.

How to Audit Your Current AI Search Visibility

Understanding your current AI Search performance provides the foundation for strategic optimisation. This audit process reveals both your existing strengths and the most promising opportunities for improvement.

The manual audit approach requires systematic testing across major AI platforms. Begin by identifying 20-30 queries relevant to your business across different categories:

• Direct product/service queries
• Comparison queries (“best X for Y”)
• Problem-solving queries (“how to solve X”)
• Industry-specific questions

Test each query across ChatGPT, Google AI Overviews, Perplexity, and Google’s AI Mode. Document which tools cite your content, where you appear in responses, and which competitors receive mentions.

Automated audit tools like Profound, BrightEdge, or similar platforms can accelerate this process significantly. These tools provide visibility scores showing what percentage of relevant queries result in your content being cited.

For example, a visibility score of 79% means that across 100 relevant AI searches, your content gets referenced in approximately 79 responses. This metric provides a baseline for measuring improvement over time.

Topic-specific analysis reveals optimisation priorities. Most audit tools break down performance by topic categories, showing where you’re strong versus where improvement opportunities exist. A business might discover they’re consistently cited for product features but never mentioned for implementation guidance.

Competitor citation analysis identifies successful content patterns. Examine which specific articles and content formats your competitors use to secure AI citations. Look for commonalities in structure, length, formatting, and approach across consistently cited content.

The citation source audit reveals platform preferences. Track whether your citations come from your own website, third-party mentions, user-generated content, or social media. This analysis guides content distribution strategy and identifies gap areas requiring attention.

Performance tracking metrics should include:

• Overall visibility score across AI platforms
• Citation volume by topic category
• Citation source distribution (owned vs earned media)
• Competitor citation share within your industry
• Response positioning (first mention vs later references)

This audit process typically requires 4-6 hours for manual approaches or can be completed in under an hour using specialised tools. The insights gained form the strategic foundation for all subsequent AI search optimisation efforts.

How to Create Content That Ranks in AI Search

Creating content that consistently gets cited in AI Search requires understanding both what AI tools prefer and what makes content genuinely valuable to users. The intersection of these factors determines citation success.

The E-E-A-T framework provides the foundational structure for AI-optimised content. Google’s evolution from E-A-T to E-E-A-T (adding Experience to Expertise, Authoritativeness, and Trust) reflects what AI tools increasingly prioritise when selecting content to cite.

Experience-driven content creation separates winners from generic content. Instead of asking ChatGPT to write about “best practices for heat pump installation,” we interview our engineers about specific client projects. What went wrong? What unexpected challenges arose? Which solutions worked better than anticipated?

This approach creates content that AI tools cannot replicate because the information simply doesn’t exist elsewhere. When someone asks about heat pump installation challenges, the AI tool cites our content because we’re the only source documenting those specific experiences.

Answer-first architecture aligns with how AI tools extract information. Structure your content to provide clear, quotable answers immediately:

• Opening summary that directly answers the main question
• Credibility indicators (author expertise, methodology, verification)
• Primary recommendation with clear reasoning
• Detailed supporting information and comparisons
• Implementation guidance and practical next steps

The query fan-out strategy recognises how AI tools break complex questions into multiple searches. When someone asks about “the best project management software for creative agencies,” the AI tool simultaneously searches for:

• “project management software creative agencies“
• “best project management tools 2025“
• “creative agency software recommendations“
• “project management features designers need“

Creating content that addresses multiple related angles increases citation probability across these parallel searches.

Formatting for extraction makes your content AI-friendly:

• Clear hierarchical headings (H1, H2, H3) that describe content sections
• Bullet points and numbered lists for easy information extraction
• Comparison tables that structure decision-making information
• FAQ sections addressing related questions
• Statistical data points that support recommendations
• Schema markup to reinforce content structure

Content depth without complexity means comprehensive coverage presented accessibly. Create detailed content that covers topics thoroughly while maintaining readability and clear information hierarchy.

The methodology transparency requirement means explaining how you reached conclusions. AI tools consistently favour content that documents research processes, comparison criteria, and decision-making frameworks.

Regular content updates maintain relevance for time-sensitive topics. AI tools prefer content with current dates and fresh information, particularly for product comparisons and industry recommendations.

Cross-platform content distribution amplifies citation opportunities. Create core content for your website, then develop variations for industry publications, contribute to relevant forums, and engage in social media discussions around your topics.

This multi-platform approach increases the likelihood that AI tools encounter your expertise across multiple sources, reinforcing your authority and improving citation probability.

“And I use the Exposure Ninja team for a couple of businesses that I’ve bought, and the content that the team produces is so far superior to what I can produce on my own using ChatGPT or using Claude, it’s unreal.”

Tim Cameron-Kitchen
Founder

Next Steps

Implementing AI Search Optimisation requires a systematic approach that builds on your existing content marketing foundation while introducing new strategic elements.

Week 1: Conduct Your AI Search Audit

Begin with a focused audit of your current AI Search visibility. Select 15-20 core queries relevant to your business and manually test them across ChatGPT, Google AI overviews, and Perplexity. Document current citation patterns and identify immediate opportunities.

Week 2: Analyse Competitor Citations

Examine which content formats and sources consistently get cited for your target topics. Look for patterns in structure, length, and approach across successful content. This analysis provides your content template for future optimisation.

Week 3: Develop Your Content Strategy

Based on audit findings, prioritise 3-5 topic areas where you can realistically establish citation dominance. Focus on areas where you have genuine experience and expertise rather than attempting to compete across all possible topics.

Week 4: Create Your First AI-Optimised Content

Develop one comprehensive piece of content following AI Search best practices. Include personal experience, clear formatting, immediate answers, and comprehensive topic coverage. This becomes your template for future content development.

Month 2: Scale Content Production

Establish a sustainable content creation process that incorporates AI Search Optimisation principles. Whether creating content in-house or working with external teams, ensure consistent application of AI Search best practices.

Month 3: Monitor and Optimise

Track citation performance across AI platforms and adjust your approach based on results. AI Search preferences evolve rapidly, requiring ongoing attention and adaptation.

Implementation priorities should focus on:

• Content where you have unique expertise or experience
• Topics with high business impact but manageable competition
• Queries that indicate high purchase intent or qualified leads
• Areas where current AI citations favour weak or generic content

Resource allocation typically requires 20-30% additional content marketing effort initially, decreasing as processes become established and templates proven effective.

Resources Mentioned

• Profound
• Exposure Ninja’s famous website and marketing review
• Blog: How To Optimise Your Blog Content Strategy for AI Search Success
• Blog: How to Create an AI Search Optimisation Strategy in 2025
• Blog: What Is E-E-A-T and Is It Still Important for SEO?
• Elite Renewables
• Our AI Search Optimisation service
• Our AI Search Strategy consultancy service

In Conclusion

AI Search Optimisation represents the most significant shift in digital marketing since the mobile revolution.

The businesses that master these strategies now will establish competitive advantages that become increasingly difficult to challenge as AI Search adoption accelerates.

The key insight driving successful AI Search Optimisation is understanding that these tools don’t just want information — they want authority. They prefer content created by people with genuine experience over generic AI-generated alternatives. They favour clear, actionable answers over lengthy explorations. They reward comprehensive topic coverage while maintaining accessibility and structure.

Your competitive advantage lies not in gaming these systems, but in becoming genuinely authoritative in your field and presenting that authority in formats AI tools prefer to cite.

The opportunity window for establishing AI Search dominance remains open, but it’s narrowing rapidly. The businesses that act now will secure positioning that becomes self-reinforcing as industry recognition drives increased citation probability.

Start with the audit process outlined above. Understand where you currently stand, identify your strongest opportunities, and begin creating the experience-driven content that sets you apart in this new search landscape.

The future of search is already here — it’s just unevenly distributed. Make sure your business is positioned to benefit from this transformation rather than being left behind by it.

Watch This Next

Many of the changes to how Google’s usage of AI within its search results, including AI Mode and AI Overviews, was announced just a short while ago at Google I/O 2025. The annual update from Alphabet, Google’s parent company, is always a must-watch event, but especially so this year.

AI Mode, heavily covered at the event, is just the tip of the iceberg of what Google has planned — and our team summarised all the most impactful news and updates in a recent episode of The Dojo, our search marketing news podcast.

You can watch it below, or listen to the audio recording instead via your favourite podcast app, or on the podcast show notes page: How Google Changed the Future of SEO at Google I/O 2025