RESEARCH ON THE USE OF ARTIFICIAL INTELLIGENCE TOOLS IN CORPORATE ADVERTISING ACTIVITIES - BILYK, Maryna; BUNAK, Anastasiia; KUSHCH, Anastasiia (Herald of Khmelnytskyi National University. Economic sciences, 2024)
This article examines the role of artificial intelligence in marketing advertising activities, highlighting how AI-driven tools enhance efficiency and personalization. The purpose of the article is to study artificial intelligence tools in the advertising activities of the enterprise and ways of their use. Nowadays artificial intelligence is becoming increasingly popular among different companies due to its ability to automate Big Data collection and analysis, allowing them to design customised advertising campaigns based on customers preferences and behaviour. The most popular AI tools such as Google Ads, Facebook Ads, Albert and Cortex, were analysed for their impact in areas such as targeting and improving content strategies. This analysis proved their effectiveness in optimising marketing activities and evaluated their advantages and disadvantages. The AI-tools implementation was illustrated by case studies of companies like TikTok and Sephora, which demonstrated how artificial intelligence helped to enhance customer service and optimised precision of targeted ads. Despite these advantages, the article acknowledges the challenges businesses face with AI using, such as customers’ data privacy concerns, requirements for financial resources and overall data control. Despite the numerous benefits of using AI to create personalized ads, there are also a number of drawbacks and barriers to business adoption, including: cost, ethics, and manipulation. The article emphasises that artificial intelligence has the potential to become an integral part of marketing processes through improving business operations, increasing accuracy of commercial appeals and helping companies remain competitive in an evolving market landscape. Prospects for further research include ethical issues of using personal data, ways to optimize financial costs for high-value platforms, and the need for data control.