The integration of generative artificial intelligence into everyday interpersonal communication - through AI-assisted writing, real-time translation, automated summarization, and conversational suggestion tools - represents a structural transformation in the mediation of human-human communication. Unlike earlier communication technologies that mainly transmitted or stored human-generated content, generative AI actively co-produces communicative output through a system architecture that includes user interfaces, prompt and context management, large language model inference, personalization modules, disclosure controls, and post-generation human editing. This paper examines AI-mediated interpersonal communication (AIMIC) as both a social phenomenon and an implementable communication system. It analyzes three interconnected effects: linguistic convergence, redistribution of communicative labor, and challenges to relational authenticity. The revised AIMIC framework links these effects to concrete implementation layers in email assistants, messaging platforms, translation tools, collaborative writing systems, and organizational communication software. The paper argues that responsible AIMIC design requires preserving human communicative agency, making AI involvement contextually transparent, monitoring linguistic and relational impacts, and embedding governance mechanisms into the communication pipeline. The paper concludes with practical examples, evaluation recommendations, and design principles for platform developers, educators, organizations, and policymakers.