The rapid adoption of Artificial Intelligence (AI), particularly Generative AI, fundamentally reshapes the future of work and demands a critical re-evaluation of employability constructs. While traditional frameworks emphasize general competencies like problem-solving and communication, they often fail to capture the specific cognitive, technical, and ethical skills required for effective human-AI collaboration. This Integrative Literature Review (ILR) aims to critically analyze existing employability models against the emerging dimensions of AI Literacy to identify critical gaps and propose a unified framework for career development. We first analyze influential traditional employability frameworks (e.g., Yorke, Fugate) and then synthesize key competencies from recent AI Literacy studies (e.g., technical understanding, prompt optimization, critical evaluation, and ethical awareness). Our systematic analysis is structured in three phases: 1) Deconstruction of seminal employability models, focusing on core attributes (Knowledge, Skills, Attitudes). 2) Synthesis of core AI Literacy components (e.g., Prompt Engineering, Algorithmic Transparency, Critical Evaluation of Output) from the latest literature. 3) Critical Postulation: We anticipate that a significant gap exists between established generic skills and the domain-specific, ethical, and interactive demands of AI literacy. The study is expected to reveal that traditional frameworks exhibit a lack of conceptual granularity in addressing biases, ethical responsibility, and the co-creation of value with intelligent systems. Based on this anticipated finding, we will propose a Conceptual Framework for AI-Augmented Employability that repositions AI Literacy as a pivotal, transversal competence. This synthesis offers critical implications for vocational curriculum design, career psychology, and organizational Human Resource Development (HRD) strategies.