AI is changing how search works, but core SEO principles remain. Katharina Vogt explains why structure, trust, and real experience matter more than ever. Companies relying only on AI-generated content risk losing visibility.
As AI-driven search tools reshape how users find information, companies face new challenges in measuring and maintaining online visibility. Katharina Vogt, founder of Vogt digital GmbH and an expert in SEO and Google Ads, says that while AI is transforming search, the fundamentals of search engine optimization remain essential for publishers and brands seeking measurable results.
Vogt, who began her career in e-commerce and marketing at Amazon before moving into agency work and eventually launching her own consultancy, emphasizes that SEO is not obsolete. She notes that the rise of AI-powered systems adds a new layer to the search landscape, requiring content creators to consider both traditional search engines and AI models as audiences for their work.
She observes that search queries are shifting from simple keywords to more complex, individualized prompts. This evolution makes it harder for companies to track search volume and user intent using standard SEO tools. However, classic Google Search remains relevant, even as AI introduces new requirements for content structure and source credibility.
Vogt highlights that AI systems now better distinguish between sponsored and organic content, a capability that was previously lacking. She explains that structured data, clear metadata, and well-organized headings help AI and search engines interpret website content more effectively. Overloading pages with keywords or unnecessary plugins, she warns, can slow sites and reduce visibility.
As user queries become longer and more specific, traditional keyword tracking tools struggle to provide accurate data. Vogt points out that while clustering prompts by topic is possible, the sheer variety of user questions makes this approach complex. She stresses that AI aims to deliver precise answers, so content must be detailed and directly address user needs.
Trust in the source is increasingly important. Vogt says that AI platforms evaluate the trustworthiness of content using their own algorithms, making it vital for companies to be cited as reliable sources. She recommends creating specific FAQs and in-depth blog posts to address niche topics, noting that being referenced in industry directories and on platforms like LinkedIn and Instagram can also boost credibility.
Vogt cautions against relying solely on mass-produced AI content, arguing that quantity does not guarantee visibility or trust. Poor-quality AI-generated material can even damage a brand’s reputation. Instead, she advises integrating unique perspectives, original data, and firsthand experiences into content strategies to stand out as a valuable source for both users and AI systems.
She explains that companies can systematically generate and showcase their own experiences, such as customer reviews and case studies, to differentiate themselves in both B2C and B2B contexts. In B2B, combining feedback from sales teams and clients helps answer real customer questions more effectively than generic content.
Vogt believes that as AI becomes more central to search, companies must focus on differentiation. She notes that while some tools claim to measure AI-driven visibility, most are either prohibitively expensive or lack accuracy. Traditional SEO metrics like visibility index and keyword count are still useful but are losing relevance as user behavior changes.
To track performance, Vogt recommends analyzing traffic sources through analytics platforms and using UTM parameters to improve attribution, even though it remains difficult to isolate traffic from AI platforms. She adds that A/B testing is more practical in paid channels like Google Ads than in SEO, due to the risk of duplicate content and the need for unique indexed pages.
When advising clients, Vogt focuses on actionable data-such as which headlines or messages perform best-rather than trying to predict algorithm changes. She sees value in returning to classic focus groups to understand user preferences beyond what analytics can reveal.
For companies aiming to improve visibility, Vogt suggests starting with technical basics: ensuring websites are secure, mobile-friendly, and up to date. She warns against relying on personalized Google results and stresses the importance of defining clear target audiences and problems to solve.
Vogt also discusses the option for publishers to set themselves as preferred sources in Google, which can increase trust and direct traffic but is most relevant for large media brands. She cautions that overuse of such features by smaller blogs may not yield benefits and could even backfire if content is not regularly updated.
She notes that this approach can bypass normal search behavior, making it easier for loyal users to access content but potentially reducing exposure to diverse sources. Increased clicks from preferred source buttons can signal trust to AI systems and improve the likelihood of being featured in AI-generated results. Vogt emphasizes the responsibility that comes with producing large volumes of content, warning that poor-quality material can harm a brand and contribute to misinformation-a concern echoed in recent reporting on AI’s impact on advertising, such as in coverage of AI oversight in ad campaigns.
