In organizations right now, everyone is racing to build custom AI conversational assistants. We connect them to team drives, upload training manuals, and expect LLMs to "be an expert." Behind that, these agents are choking on your data. They skip critical sections of Word documents, get confused by conflicting and implicit information, and require users to write complex, multi-paragraph prompts just to get a straight forward, formatted response. Over the last 6 months, my role in Knowledge Management (KM) has shifted. I went from figuring out how humans record and share documentation to figuring out how my team can use AI to document, share, and communicate institutional intelligence. Building on top of knowledge-routing frameworks and data-loading theories pioneered by other AI innovators, I've been looking at how data hits the models that my company uses internally. What I've come up with is a conceptual blueprint that addresses these cracks. It's still a working theory right now…
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