From SEO to GEO: When Being Found Is No Longer Enough
For years, digital marketing focused on SEO: appearing as high as possible in Google results. Generative AI is changing this logic. When users receive AI-generated answers, visibility no longer means only being high in search results. It also means being used, cited, or recommended by AI.
Grossman et al. (2026) show that Google AI Overviews appeared for 52% of representative real-user queries. At the same time, AI-generated answers often use different sources than traditional Google results. The average similarity between AI Overviews and traditional Google sources was only 0.18. This means that a website can perform well in SEO and still not appear in an AI-generated answer.
This shift is especially important in e-commerce. AI Overviews appeared for 88% of product comparison queries and 92% of product questions. Generative search may therefore strongly influence the moment when consumers compare products and look for recommendations.
Chen et al. (2025) describe GEO as a shift from ranking to influence. The key question is not only how high a page ranks, but whether it shapes the AI-generated answer. Bagga et al. (2025) show that in e-commerce, systematic optimization can improve product rankings in generative shopping recommendations.
SEO is not disappearing. But SEO alone may no longer be sufficient. The new marketing question is: Will AI consider our content, brand, or product useful enough to include in its answer? In the age of generative search, visibility means being found, cited, trusted, and used by AI.
Bibliography:
Grossman, R., Liu, S., Chen, M. K., Smith, M., Borcea, C., & Chen, Y. (2026). How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews. arXiv preprint arXiv:2604.27790.
Chen, Q., Chen, J., Huang, H., Shao, Q., Chen, J., Hua, R., et al. (2025). CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines. arXiv preprint arXiv:2509.05607.
Bagga, P. S., Farias, V. F., Korkotashvili, T., Peng, T., & Wu, Y. (2025). E-GEO: A Testbed for Generative Engine Optimization in E-Commerce. arXiv preprint arXiv:2511.20867.
This post is part of the project “People and Algorithms in Organisations: Competences to Work in the Digital Environment” (DIGIT_People and algorithms), funded by the Polish National Agency for Academic Exchange (NAWA).
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