Abstract: Organizations face a persistent value gap in artificial intelligence adoption: while most have deployed AI tools, fewer than 5% generate value at scale. This article examines why traditional training approaches fail to bridge the adoption-to-impact divide and proposes an integrated capability-building framework grounded in organizational behavior research and practitioner evidence. Drawing on recent consulting experience and academic literature on technology adoption, learning transfer, and behavior change, we outline a three-stage progression—foundational knowledge, applied practice, and embedded habits—that moves beyond conventional training programs. The article presents role-specific capability development strategies, leadership modeling imperatives, trust-building mechanisms, and measurement approaches that connect learning interventions to tangible business outcomes. Case evidence from financial services, consumer goods, biopharmaceuticals, and technology sectors illustrates how targeted capability investment in high-value workflows unlocks AI's transformative potential when supported by redesigned work systems, visible executive commitment, and metrics focused on business impact rather than mere adoption rates.