AI-Driven Personalization in Organizational Communication

This study investigates the transformative impact of artificial intelligence on corporate communications, focusing on AI-powered personalization systems in business environments. Through a systematic literature review (2019-2024), the research establishes an empirical framework for evaluating these systems’ technological infrastructure. The findings reveal distinct sector-specific performance variations: the retail sector showing 58% enhanced engagement metrics, while B2B segments demonstrated 28% improvement in key performance indicators. The technological foundation comprises machine learning algorithms, natural language processing frameworks, and high-performance computing systems enabling real-time personalization. The methodology integrates the adaptive personalization framework (APF) with the multidimensional personalization model (MPM) to elucidate machine learning mechanisms. This framework supports user profiling, navigation optimization, and behavioral pattern modification, secured through distributed ledger technologies. Empirical analysis reveals the complementarity between AI and human capabilities. While AI systems excel in response velocity (mean: 4.92), human interactions demonstrate superior responsiveness (5.27) and professional competency metrics (5.32 vs. 4.87), suggesting the optimality of a hybrid model. The study culminates in a conceptual framework balancing communication scalability with personalized relevance while adhering to ethical imperatives of data protection, algorithmic fairness, and transparency protocol.