Global Marketing in the Age of Artificial Intelligence and Emerging Opportunities
DOI:
https://doi.org/10.66027/GPIM/V4I1/GPM26108Keywords:
Artificial Intelligence; Global Marketing; Predictive Analytics; Consumer Personalization; Digital Transformation; Cross-Cultural Marketing; Emerging Markets.Abstract
There is no denying the fact that the explosion in terms of the number of innovations in AI that have become available has caused a drastic shift in the world of global marketing; therefore, companies around the world need to adopt new strategies in order to take advantage of this new phenomenon. This paper will focus on the multivariate relationship between the application of AI innovations and the overall performance of global marketing initiatives. More specifically, this paper will focus on consumer personalization, predictive analysis, intercultural communication, and dynamic pricing strategies. This paper follows a mixed-methods research approach where, on the one hand, data were collected quantitatively through the administration of a survey on 312 marketing managers in 18 countries across various industries, such as retail, banking, technology, and healthcare, and, on the other hand, data were collected qualitatively from 24 marketing executives through semi-structured interviews. The results indicate that personalized consumer experience through AI technology has a positive effect on consumer engagement (β = 0.64, p < 0.001) and brand loyalty (β = 0.51, p < 0.01), but the use of predictive analytics proves highly beneficial for increasing ROI (R² = 0.73). Cross-cultural adaptation of AI is viewed as a key mediator between the ability of AI and its market penetration. Among the emerging opportunities are real-time tracking of consumer sentiment, conversational marketing, and influencer marketing based on AI. In this study, a new GAIMIF model combining technological, cultural, and strategic elements of the AI integration process in global marketing operations is offered. Some of the limitations of this paper include inherent biases associated with self-reported data and the problem of obsolescence. More studies need to be conducted to analyze the long-term effects of AI.
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Copyright (c) 2026 Rafael Oliveira, Mariana Costa (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
