Abstract: As artificial intelligence reshapes labor markets globally, organizational leaders face a fundamental strategic question: which capabilities truly predict performance in AI-augmented work environments? While public discourse fixates on job displacement projections—the World Economic Forum estimates 92 million job losses against 170 million new roles by 2030—emerging research reveals a critical distinction between superficial AI adoption and transformative capability development. This article synthesizes evidence from leading academic institutions and consulting firms to demonstrate that technical AI proficiency alone provides minimal competitive advantage. Instead, six meta-competencies—adaptive learning capacity, deep AI comprehension, temporal leverage, strategic agency, creative problem-solving, and stakeholder empathy—distinguish high performers from surface-level experimenters. Drawing on cost-benefit frameworks from McKinsey, capability models from Harvard and Stanford, and organizational case studies spanning healthcare, professional services, and manufacturing, we provide evidence-based guidance for developing sustainable AI fluency. The synthesis reveals that return-on-investment literacy for automation decisions has emerged as a core executive competency, separating productive implementation from expensive overhead creation.