Digital Transformation, Corporate Data and Gen AI: LLMs and the Challenge of Retrieval

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:

  • LLM hallucinations caused by poor or missing retrieval pathways

  • Metadata and knowledge management gaps that reduce AI effectiveness

  • The need for structured taxonomies and information architectures to support AI systems

  • 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.
👉 Read the full article here

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.