What signal do you trust when evaluating AI search visibility

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• Part 1SEO Under Siege: 2025’s Collapse of Google Traffic

  Organic traffic dropped 40% in 2025 as AI search reshapes discovery. Malte Landwehr, CMO and Chief Product Officer at Peak AI, brings enterprise experience from Idealo (Europe's largest price comparison site) and deep insights from reverse-engineering ChatGPT's shopping functionality. The discussion covers grounding optimization strategies that influence AI model source selection, measurement frameworks replacing click-based attribution with self-reported data and visibility tracking, and multi-platform authority building across Reddit, YouTube, and third-party sources that AI models consistently cite for commercial queries.

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• Part 2One SEO habit teams need to unlearn immediately

  Most SEO teams obsess over individual AI prompts instead of topic clusters. Malte Landwehr, CMO and Chief Product Officer at Peak AI, advocates for self-reported attribution as the most reliable signal for measuring AI discoverability success over traditional traffic metrics. He outlines a systematic approach to prompt research using customer support tickets, sales transcripts, and website search data to understand authentic user language rather than vendor terminology. The discussion covers topic-based prompt clustering strategies and multi-persona funnel optimization for comprehensive AI visibility measurement.

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• Part 3The most dangerous thing tools encourage when they automate content at scale

  AI content automation creates a dangerous disconnect between brand messaging and user intent. Malte Landwehr, CMO and Chief Product Officer at Peak AI, shares proven methodologies for maintaining content authenticity while scaling AI-driven search strategies. The discussion covers self-reported attribution frameworks for measuring AI discoverability beyond traditional traffic metrics, prompt diversification strategies that leverage real customer language from support tickets and sales transcripts, and topic-based visibility measurement approaches that account for multiple user personas across different funnel stages.

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• Part 4Where should brands actually invest today?

  73% of brands struggle to measure AI-driven search visibility effectively. Malte Landwehr, CMO and Chief Product Officer at Peak AI, shares proven methodologies for tracking discoverability beyond traditional traffic metrics. He outlines self-reported attribution frameworks for measuring AI search impact and systematic prompt selection strategies that leverage existing customer data from support tickets, sales transcripts, and website interactions to optimize for multiple personas across different funnel stages.

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  Part 5What signal do you trust when evaluating AI search visibility

  AI search visibility tracking faces a 70% attribution gap compared to traditional SEO metrics. Malte Landwehr, CMO and Chief Product Officer at Peak AI, has developed systematic approaches for measuring discoverability across AI-powered search platforms where conventional traffic data falls short. The discussion covers self-reported attribution methodologies for capturing AI-driven conversions and prompt diversification strategies that leverage customer support data, sales transcripts, and user behavior patterns to optimize for topic clusters rather than individual queries.

• Part 6The clearest sign a company is not ready for AI-driven discovery

  Most companies lack systematic approaches to measure AI-driven search visibility beyond traditional traffic metrics. Malte Landwehr, CMO and Chief Product Officer at Peak AI, shares proven methodologies for evaluating discoverability across AI-powered search platforms. The discussion covers self-reported attribution frameworks for tracking AI-generated leads, prompt diversification strategies that account for user intent variations across sales funnel stages, and systematic visibility monitoring approaches that aggregate data across multiple prompts rather than relying on single-query assessments.

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