Glossary of KM terms

Agentic AI

AI capable of pursuing goals and taking actions across systems, rather than only generating responses. This increases the importance of permissions, authority, context, provenance and governance.

AI-ready knowledge

Knowledge that has sufficient context, quality, structure, governance and accessibility to be used reliably by AI—not simply content to which AI has access. 

Collective intelligence

The process by which a large group of individuals gather and share their knowledge, data and skills for the purpose of solving issues. Learn more

Corporate/Institutional memory

Knowledge retained by the organization beyond the individuals, teams or technologies that originally created or held it. 

Enterprise search

The capability to find relevant information across an organization's different systems, repositories, and content sources through a unified search experience. Learn more

Governance

The policies, roles, accountabilities and controls that determine how knowledge is created, owned, accessed, maintained, trusted and retired. Learn more

Knowledge architecture

The way an organization structures, organizes, classifies, and connects its knowledge so that people and AI can find, understand, and use it effectively.

Knowledge asset

Recorded or embodied organizational knowledge with continuing value—for example policies, precedents, research, expertise, decisions and lessons learned.

Knowledge governance

A framework of ownership, policies, roles, processes, and controls that keeps organizational knowledge trustworthy, current, appropriately accessible, and manageable throughout its lifecycle.

Knowledge graph

A machine-readable network connecting entities, concepts and their relationships.

Knowledge infrastructure

The interconnected system that enables an organization to create, preserve, organize, connect, find, trust and use what it knows. Learn more

Knowledge layer

A structured framework that connects enterprise information with context, relationships, governance and metadata so both people and AI systems can retrieve trusted, meaningful knowledge rather than isolated data.

Knowledge lifecycle

The processes through which knowledge is created, reviewed, maintained, updated, preserved and eventually retired. Learn more

Interoperability

The ability of different people, processes, systems, and machines to exchange and use information while preserving its meaning and context. Learn more

Metadata

Structured information describing knowledge—for example subject, author, owner, date, status, jurisdiction, source or review date. Learn more

Ontology

A formal model of concepts and the relationships between them, enabling richer interpretation of meaning and context. 

Provenance

Information about the origin and history of knowledge that helps establish authenticity, context and trust. 

RAG Retrieval-Augmented Generation

An approach in which an AI system retrieves relevant external information and provides it as context when generating a response. Learn more

Semantic layer

A shared layer of business meaning that helps people and systems interpret data and knowledge consistently across different sources. 

Taxonomy

A controlled vocabulary or classification structure used to categorize knowledge consistently.