Marketing automation platforms often struggle to deliver context-aware, personalized interactions in real-time. This paper proposes a novel conceptual framework for a next-generation omnichannel marketing automation platform to address this gap. Three different architectures are proposed; first, a recommendation engine based on product-customer relationships with temporal and price sensitivity modeling. The second encompasses a dynamic channel optimization module that employs Reinforcement Learning (RL) to determine the most effective communication channel for each user in real-time, and the third is a generation module that uses a Retrieval-Augmented Generation (RAG) architecture with Large Language Models (LLMs) to propose relevant and executable marketing actions. The three architectures work in an interconnected system in order to deliver personalized and context-aware marketing communications. The main contribution of this framework is to integrate transparent models, decision processes, and mechanisms for an ethical and explainable AI. Although still in progress, this research is expected to provide a blueprint for intelligent omnichannel marketing automation, with the potential to enhance personalization, user engagement, and the effectiveness of marketing communications, in e-commerce, supermarket, fashion, healthcare and wellness, and furniture retailers.