Why GenAI Pilots Fail to Scale: Insights From Seth Earley, Thomas Blumer, and Heather Eisenbraun
Many organizations celebrate early success with generative AI pilots only to discover that those promising proofs of concept cannot scale across the business. In this article, Seth Earley, Thomas Blumer, and Heather Eisenbraun explain why most GenAI initiatives stall and outline the architectural foundations needed to achieve reliable enterprise performance.
Their perspective highlights a consistent pattern across industries: the issue is not the AI model but the information architecture surrounding it. Pilots succeed because they operate in controlled, curated environments. At scale, organizations face conflicting content sources, inconsistent terminology, fragmented ownership, and metadata gaps. Without the right structure, AI systems produce answers that seem plausible but are often wrong, eroding user trust and limiting adoption.
Key Takeaways
Context is the engine of accuracy. Metadata, taxonomy, content identity, and relationship mapping determine whether GenAI retrieves information that is current, authoritative, and relevant.
Progressive enhancement is essential for scale. Successful teams begin with a small set of core metadata fields, use AI to accelerate tagging, and refine models continually as adoption grows.
Governance must evolve for AI. Traditional slow review cycles are not enough. Effective AI governance requires continuous monitoring, rapid correction, and feedback loops that strengthen performance over time.
Together, the authors reinforce a central message: AI success is an information problem before it is a technology problem. Organizations that invest in high quality data, strong metadata, and modern governance practices will accelerate every AI use case that follows.
[Read the article on Cognitive World]