AI search is transforming how users find information and how brands need to approach visibility. In a recent AirOps webinar, Mark Williams-Cook (Director at Candour and founder of AlsoAsked) joined host Josh Spilker to unpack what's really changing with AI-driven search and how SEO teams can adapt their strategies for success.

Top Takeaways

Best Practices and Key Learnings

AI Mode is changing the rules, but actionable strategies still exist for teams ready to adapt. Here are the most important best practices and lessons from the discussion:

Prioritize Technical Health and Content Quality

The foundation of visibility in both traditional and AI search remains solid technical SEO and high-quality content. These elements create the trust signals that determine whether your site is eligible for AI features.

• Focus on technical fundamentals like site speed, crawlability, and clean HTML structure to ensure AI systems can easily access your content.
• Invest in creating comprehensive, authoritative content that clearly addresses user needs and demonstrates expertise in your field.

"The core of the actual actions you should be doing to improve your coverage, I don't feel, have changed massively." — Mark Williams-Cook

Build Off-Site Authority and Positive Sentiment

For AI search, how others talk about your brand is becoming as important as your own content. Building a strong presence across multiple authoritative sites creates more opportunities to be cited in AI-generated answers.

• Develop a proactive digital PR strategy that generates mentions and backlinks from trusted industry sources.
• Actively manage your brand's online reputation, encouraging positive reviews and addressing negative sentiment that could impact AI visibility.

Map Conversational Queries and Intent

Understanding how users express their needs conversationally is critical for AI search optimization. Identifying which queries trigger external grounding helps you focus on opportunities where your content can influence results.

• Expand keyword research to include natural language variations and multi-parameter queries that reflect how people interact with AI interfaces.
• Develop systems to identify which topics and questions cause AI systems to seek external verification rather than answering from internal knowledge.

"I think of the LLM as almost like a universal intent decoder, so it doesn't matter how you ask the question, the LLM kind of gets it from its language graph." — Mark Williams-Cook

Don't Over-Invest in Schema for AI Mode

While schema markup remains valuable for traditional search features, it's not a direct lever for AI Mode visibility. Prioritize other optimization tactics that have more direct impact on how AI systems evaluate and cite sources.

• Continue implementing schema for its benefits in traditional search and knowledge graph integration.
• Balance schema implementation with higher-impact activities like content quality improvements and authority building.

Track Brand Visibility, Not Just Rankings

As traditional ranking metrics become less relevant, shift your measurement focus to broader indicators of brand presence and citation frequency across AI-generated answers.

• Develop new KPIs that track mentions and citations across multiple platforms and AI interfaces.
• Look beyond traffic to measure how frequently your brand appears as a source in AI-generated content.

Turning AI Mode Insights into Actionable Results

Success in the AI search era requires balancing traditional SEO fundamentals with new strategies for off-site authority and conversational content. By focusing on technical excellence, brand presence across authoritative sources, and identifying high-impact grounding opportunities, you can maintain visibility regardless of how search interfaces evolve.

Book a strategy session to learn how AirOps can help you implement these insights.

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