A UNIFIED AI FRAMEWORK FOR CROSS-SECTOR DIGITAL MARKETING UNDER SAUDI VISION 2030
DOI:
https://doi.org/10.18623/rvd.v23.6458Palabras clave:
Artificial Intelligence, Digital Marketing, Saudi Vision 2030, Customer Experience, Personalization, Generative AI, Data Governance, Cross-Sector Marketing, Digital TransformationResumen
AI technology is revolutionizing digital marketing by changing its nature from merely a function designed for the implementation of marketing campaigns to an instrument capable of providing insight into the customers, tailoring marketing activities to specific clients, developing targeted marketing content, organizing marketing channels and controlling the whole process in the context of performance management. The current paper is aimed at creating a comprehensive and multi-layered AI-based framework for cross-sector digital marketing, specifically as it is needed in Saudi Arabia in connection with the Saudi Vision 2030. There is an actual demand among Saudi entities in such industries as tourism, retail, banking, education, healthcare, logistics, entertainment, and government services to use AI tools for marketing activities with the same set of standards and guidelines to be able to realize the national benefit of the efforts. In accordance with the structured narrative review methodology, the research reviews the existing body of academic literature about AI-based marketing, customer experience management, generative AI, data management, platform ecosystem, and Saudi digital transformation published between 2020 – 2025. As a result, the article identifies ways AI-based marketing could help the Saudi country reach its goals, including increased competitiveness, service penetration, SME development, client inclusion, and evidence-based communication. The review findings indicate that in order to achieve the desired outcome, the parties will have to ensure data readiness, comply with the data governance rules, monitor the process, be able to work with the Arabic language, and employ ethical modelling and learning.
Citas
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