Many generative AI (GenAI) initiatives fall short because organizations misjudge the technology’s readiness and fail to align projects with business outcomes. This article explores the most common reasons GenAI projects stall: immature data infrastructure, lack of use case clarity, and limited understanding of how GenAI integrates into existing workflows. For digital leaders, success starts with disciplined scoping, structured data foundations, and a robust knowledge architecture that grounds GenAI in business context. With the right governance and operational alignment, GenAI can improve decision velocity, augment customer-facing processes, and scale internal expertise—turning early experimentation into meaningful business value.
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