It investigates the relationship between the digital maturity of business-to-business organizations and their ability to implement effective personalized marketing communications in the context of evolving industrial paradigms associated with Industry 4.0 and Industry 5.0. The growing complexity of digital ecosystems, the increasingly important role of data-driven decision-making, and the rapid adoption of artificial intelligence technologies fundamentally transform marketing processes and customer engagement strategies in B2B environments. In today’s time, companies face the challenge of synchronizing technological capabilities with organizational readiness to achieve scalable and meaningful personalization across multiple digital touchpoints. The research is based on a qualitative analytical methodological approach combining comparative analysis, synthesis, and systematization of conceptual frameworks related to digital maturity and personalized marketing. The research also uses structural and functional analysis to explore how marketing systems are undergoing a transformation under the impact of artificial intelligence and predictive analytics. This method makes possible the identification of important dimensions that are driving the shift from traditional reactive marketing models to proactive and predictive personalization strategies. The implications of the research indicate that digital maturity plays a significant role in the effectiveness of personalization for B2B communications. Higher-maturity organizations integrate digital technologies more effectively, have better data governance, and better alignment of marketing, sales, and IT functions. The results imply a clear progression from basic digital adoption toward advanced predictive personalization models enabled by artificial intelligence. A conceptual framework is proposed whereby personalization on the basis of digital maturity is divided into four interrelated levels: foundational, transformational, executive, and outcome-oriented. This model is built around the Stimulus–Organism–Response logic, explaining how external technological stimuli impact organizational capabilities and how this subsequently affects customer responses. These findings have practical implications for B2B enterprises that want to optimize their digital transformation initiatives. The proposed framework can be used as a diagnostic and planning tool for assessing organizational readiness for AI-driven personalization. As a result, it also offers strategies to reduce internal fragmentation, increase cross-functional collaboration, and ensure optimal cognitive and operational workloads within a marketing ecosystem. The proposed approach enables the organization to make better use of their resources, enhances their customer engagement strategies, and contributes to the development of sustainable competitive advantages in an ever-more digitalized market landscape.