Optimizing content marketing strategies based on search engine algorithms and artificial intelligence to increase semantic relevance and SEO effectiveness

Abstract

The integration of generative AI into search engine algorithms presents fundamental challenges for contemporary content marketing strategies. This study aims to quantify the impact of artificial intelligence on SEO performance and to develop a composite index system for optimizing content marketing in AI-driven search environments. This study employs content analysis of secondary data from leading industry platforms. In addition, the current research identifies interdependencies among key performance metrics and applies multifactor aggregation using min–max normalization. Predictive modeling is used to assess AI adoption trajectories, accounting for the volatility associated with the rollout of AI Overviews. The analysis includes an interregional comparative assessment of the United States and the European Union, incorporating institutional differences in antitrust regulation. The empirical dataset comprises more than 300,000 keywords, over 10 million queries, and large-scale clickstream data from Semrush and Ahrefs spanning 2022–2025. The AI Search Transformation Index (ASTI = 0.547) indicates a moderate level of adaptation of the search ecosystem to generative AI. The PEAIO visibility loss ratio (34.74%) captures a substantial decline in organic click-through rates for top-10 positions attributable to AI Overviews. In the US, the Traffic Redistribution Index (TRI) declined by 15.3% reflecting increased click migration toward Google-owned properties and a rise in zero-click searches to 58.5%. An AI citation anomaly was identified, whereby pages ranking in positions 21–50 exhibit a 64.7% higher likelihood of being cited in AI-generated responses than those in the top five results. The composite Global Score of 56.5 suggests moderate market readiness for AI-dominated search, alongside relatively high elasticity in retaining organic traffic. The proposed methodology enables systematic diagnostics of SEO vulnerabilities, cross-regional benchmarking of content strategies as well as the development of data-driven optimization frameworks. Strategic implications include adopting hybrid positioning strategies that balance traditional CTR optimization with AI citation visibility. Thereby it entails increasing semantic content density and diversifying traffic acquisition through platforms with higher AI citation propensity.