From knowledge repositories to knowledge infrastructure how AI-enabled and radical knowledge management reduce cost,risk,and knowledge loss

Abstract

Organizations continue to invest heavily in knowledge management (KM), yet many fail to realize measurable business value. The primary cause is not technological immaturity but a persistent mismatch between how knowledge is managed and how work is actually performed. This article argues that meaningful cost reduction and operational durability emerge only when three elements are combined: (1) AI-enabled knowledge management platforms, (2) Radical Knowledge Management (Radical KM) principles that give precedence to workflow and decision logic over documentation volume, and (3) Knowledge Retention Boards (KRBs) as a structured mechanism for capturing institutional knowledge. Drawing on applied KM practice within the broader information management tradition, the article reframes knowledge not as static content but as operational infrastructure, capable of reducing training costs by 20–40%, mitigating risk, and preserving expertise at scale.