Generative AI and Large Language Models (LLMs) are transforming the digital landscape—but without the right data foundation, their true potential remains out of reach. Many organizations are discovering that deploying LLMs is not just a technology challenge; it’s a data problem.
Success with Generative AI hinges on retrieval — the ability to find, structure, and use the right corporate knowledge at the right time. Yet most enterprises struggle with fragmented data, inconsistent metadata, and knowledge gaps that limit LLM performance.
Key challenges highlighted include:
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LLM hallucinations caused by poor or missing retrieval pathways
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Metadata and knowledge management gaps that reduce AI effectiveness
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The need for structured taxonomies and information architectures to support AI systems
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Governance and content operations as critical enablers, not afterthoughts
Enterprises that invest now in organizing their knowledge ecosystems — with clear governance, strong metadata, and retrieval-focused architectures — will lead the next phase of digital transformation powered by AI.
Read the full article on CustomerThink to explore why a strong information strategy is the real key to GenAI success.
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