Webinar Recap: Is Your SEO Strategy Ready for AI Search? with Steve Toth

Search is being fundamentally rechanged by AI. In this webinar, Steve Toth (SEO Notebook, Notebook Agency) joined Josh Spilker (AirOps) to explore how brands can adapt to the rise of large language models, optimize for AI-driven search, and build scalable content workflows.

Top 5 Takeaways

1. AI Search Traffic Converts at a Significantly Higher Rate
   Traffic from LLMs like ChatGPT converts up to 22x higher than traditional Google traffic, making each visit more valuable even as overall volume remains lower.

2. Established Brands Dominate, But Fan-Out Queries Create New Opportunities
   While big brands have an initial advantage in AI search, query fan-out enables niche and emerging players to surface in highly personalized results.

3. Content Structure and Retrieval Are Crucial for AEO
   Structuring content in clear, declarative passages (100–300 tokens) increases the likelihood of being cited by LLMs and appearing in AI-driven overviews.

4. Truthful Brand Representation in LLMs Is the Next Competitive Frontier
   Ensuring LLMs accurately reflect your product's capabilities is essential—visibility alone is no longer enough to win qualified conversions.

5. Repurposing Dropped Blog Content into Video Boosts AI Visibility
   Transforming declining Q&A blog posts into video scripts can recapture traffic and increase the likelihood of LLM citations.

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​Best Practices and Key Learnings

Staying ahead in AI search requires a blend of technical structure, workflow innovation, and brand vigilance. Here are the most actionable strategies from the discussion:

Prioritize High-Value AI Search Traffic

• Focus on optimizing for LLM-driven traffic, which, while lower in volume, delivers higher intent and conversion rates.

• Design landing pages and CTAs for users arriving from AI platforms, ensuring they quickly find answers to their deal-breakers.

"You're able to qualify the solution that you want way better with an LLM than you ever were on Google." — Steve Toth

Structure Content for Retrieval and Freshness

• Break content into clear, declarative passages (100–300 tokens) to maximize retrievability by LLMs.

• Refresh comparison and decision-focused pages regularly, adding dynamic dates and specific user criteria.

• Use schema markup and FAQs to improve chunk-level extraction.

"Having all of your information condensed in a very clear and declarative way in 100 to 300 token chunks on your website is...the best way for an LLM to quickly understand, like what those passages mean and how they relate, relate to the query, and how they support that reasoning journey." — Steve Toth

Monitor and Correct LLM Brand Representation

• Implement workflows to audit how LLMs and AI search tools describe your brand and features.

• Identify and update outdated or negative third-party content that may influence LLM outputs.

• Catalog and prioritize sources that LLMs pull from to ensure accurate, up-to-date information.

Repurpose and Scale Content for AI Visibility

• Identify Q&A blogs or pages that have lost traffic due to AI overviews and repurpose them into YouTube video scripts.

• Use AI-driven prompts for watch time optimization and engagement loops to increase the likelihood of citation.

• Build automated workflows (regex, GSC data, AI models) to scale this process across your content library.

Integrating AI Search Insights into Your SEO and Content Strategy

The rapid evolution of AI search is reshaping the path from discovery to conversion. To stay competitive, brands must adopt a dual approach: engineering content for LLM retrievability and ensuring brand truth alignment across all digital touchpoints. This means not only optimizing for visibility but also for accuracy and conversion readiness.

Building workflows to monitor, refresh, and repurpose content is essential for maintaining relevance as AI overviews and LLMs compress the user journey. Teams that proactively update their brand's digital footprint and leverage automation for content refresh will be positioned to capture high-value, AI-driven traffic. Steve Toth's AI notebook workflow shows how one SEO turns LLM signals into scalable content.

Final Thoughts

AI search is rewriting the rules of SEO, putting a premium on content structure, brand accuracy, and workflow agility. By focusing on LLM retrievability, truth alignment, and innovative content repurposing, your team can drive more qualified conversions and stay ahead as the search landscape evolves.

Want to accelerate your AI search strategy? Book a demo with AirOps to see how you can scale your most ambitious SEO and AEO workflows.

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Frequently asked questions

Who is Steve Toth?

Steve Toth is the SEO expert behind SEO Notebook and Notebook Agency. He advises brands on AI search and SEO, and he joined AirOps to break down how LLMs reshape content strategy.

How does Steve Toth use LLMs for SEO?

Steve Toth treats LLMs as high-intent discovery channels that convert far better than traditional search. He structures content into clear, declarative passages of 100 to 300 tokens so LLMs can retrieve and cite it.

What is Steve Toth's AI notebook approach to content?

Steve Toth's AI notebook approach turns declining Q&A blog posts into video scripts and repeatable workflows. This recaptures lost traffic and raises the odds that LLMs cite the brand.

AirOps for AI search readiness

AirOps helps SEO and content teams turn AI search insights into action. The platform surfaces how LLMs cite your brand, and Quill runs the Playbooks that close the gaps.

See how AirOps improves your AI search visibility. Book a demo.