Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
Knowledge management strategy used to be a plan for organizing documents. Now it’s a plan for whether your AI will work at all. The very same elements of the strategy that make knowledge accessible to employees (governance, consolidation, quality) are exactly what AI agents need to provide...
Knowledge management best practices used to be about helping employees find the right document. But times are changing, and now the stakes are much higher: will your AI provide the correct answer or a definitively wrong one? The very same practices that make knowledge useful to people (governance,...
Virtually every organization has a data governance policy. But far fewer companies actually manage that data by the time an AI agent retrieves it. This gap seems insignificant until you run into “broken” AI projects. The model is fine, the pipeline works, but the data feeding it is unmanaged,...
When enterprises entrust AI agents with real-world capabilities and decisions (such as responding to customers or processing claims), an urgent question arises: How can we trust what the agent does? AI governance is the answer. It is a framework of policies, controls, and oversight that ensures AI...
An AI knowledge base does more than just store articles. It’s a system that understands what’s written in those articles. Whereas agents used to have to enter keyword searches, an AI-powered knowledge base now finds the right content on its own, answers questions in natural language, and provides...
The right knowledge base software for customer support does two things at once: it helps support agents respond faster while also powering the self-service tools and AI that customers increasingly use before calling support. But in 2026, more attention is being paid to the second task because that...
Not every knowledge management system serves the same purpose. And that makes perfect sense, since some are designed to store documents, while others are designed to help customers find answers on their own. Lumping them all under the single term “knowledge management system” obscures the...
We’re convinced that absolutely every large company has a massive amount of data, but when it’s actually needed, no one can find it. CRM systems store customer history, ERP systems store financial transactions, document systems store contracts and regulations, and email contains agreements that...
Imagine a customer call that starts with a simple question about a return. What does your agent do? Most likely, they spend several minutes scrolling through tabs in search of the right document. But what’s happening to the customer in the meantime? They hear a pause, perhaps some pleasant music...
Every enterprise possesses vast amounts of knowledge yet spends enormous effort maintaining it manually. Some people tag documents; others update articles that became outdated a month ago; still others answer the same questions over and over again because the answer exists but is hard to find....
When a company implements enterprise RAG, it has perfectly understandable expectations that the AI will provide accurate and up-to-date answers based on corporate knowledge. But instead, the company sees confident yet incorrect answers, inconsistencies across interactions, and reliance on outdated...
Do you know the two main ways to incorporate your company’s knowledge into a language model? You can either retrieve it on-demand (RAG) or “bake” it directly into the model (fine-tuning). But if you make the wrong choice, you’ll end up with a bloated budget, outdated answers, or the need to...