Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 16 4 2024 10 01 Evaluation of the effectiveness of implementing artificial intelligence in the Google Advertising service 79 99 99052 10.22059/jitm.2024.99052 EN Оlena Chukurna Professor, Department of Management and Marketing, State University of Intelligent Technologies and Telecommunications – Kuznechna Street, 1, Odesa, Odesa region, 65000, Ukraine. Tetiana Tardaskina Аssociate professor, Department of Management and Marketing, State University of Intelligent Technologies and Telecommunications – Kuznechna Street, 1, Odesa, Odesa region, 65000, Ukraine. Yuliia Tereshko Associate Professor of the Department of Digital Technologies and Design- Analytical Solutions, Technical University Metinvest Polytechnic LLC – Pivdenne highway, 80, Zaporizhzhya, Zaporizhzhya region, 69008, Ukraine. Evgene Kholostenko Department of Management and Marketing, State University of Intelligent Technologies and Telecommunications – Kuznechna Street, 1, Odesa, Odesa region, 65000, Ukraine. Viktoria Kofman Department of Management and Marketing, State University of Intelligent Technologies and Telecommunications – Kuznechna Street, 1, Odesa, Odesa region, 65000, Ukraine. Leonid Pankovets Master of Management, postgraduate student of the third level of higher education, majoring in «Economics», State University of Intelligent Technologies and – Kuznechna street, 1, Odesa, Odesa region, 65000, Ukraine. Journal Article 2024 10 16 This paper examines the effectiveness of implementing artificial intelligence (AI) in the Google Ads advertising service. The study analyzes the advantages and disadvantages of AI integration, focusing on attribution models and end-to-end analytics. The findings show that traditional metrics, such as CTR, CPC, and ROI, used to evaluate advertising campaign performance, exhibit significant statistical errors when AI tools are applied, with errors reaching up to 35%, exceeding typical business margins. A comparative analysis in the construction industry highlights discrepancies of 10% to 35% between traditional and AI-driven models. The study concludes that universal AI algorithms often fail to account for industry-specific dynamics, leading to inaccurate evaluations. The practical significance of this research lies in proposing an alternative approach that combines traditional evaluation methods with AI-based tools, offering a more reliable framework for assessing campaign effectiveness https://jitm.ut.ac.ir/article_99052_c3eb3792aa410a4cfa0efa926f3f4d96.pdf