Why Generative AI Projects Fail and How Business Leaders Can Turn the Tide

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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Meet the Author
Seth Earley

Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.