The knowledge retention glossary

Knowledge retention is the practice of capturing and preserving the expertise, context, and decisions employees build, so an organisation keeps that knowledge when people leave or change roles.

Corporate amnesia is what happens when an organisation repeatedly loses and relearns knowledge because the reasoning behind past work leaves with the people who did it.

Institutional knowledge is the collective, often undocumented know-how a company accumulates over time: how things are done, why decisions were made, and who knows what.

A knowledge digital twin is an AI model of a role or person, trained on their real work, that teammates can question after that person leaves or changes roles.

Knowledge management is the discipline of creating, organising, sharing, and maintaining an organisation's knowledge so the right information reaches the right people.

Enterprise AI readiness is how prepared an organisation's knowledge and data are for AI: well-governed, permissioned, and accessible enough for AI tools to give accurate, safe answers.

Retrieval-augmented generation (RAG) is a technique where an AI model fetches relevant information from a knowledge source at query time and uses it to ground its answer.

The Model Context Protocol (MCP) is an open standard that lets AI assistants and agents connect to external tools and data sources through a common interface.

Knowledge coverage is the measured state of what an organisation actually knows about its critical work: what has been verified, what needs re-verification, what criteria were never recorded, what is contradictory, what has no owner, and what only one person knows.

A knowledge gap is a piece of knowledge that critical work depends on but that the organisation has never recorded, verified, or assigned an owner to.

A verification loop is the governed path that turns an identified knowledge gap into an approved, source-traceable answer: identify the gap, name the verifier, ask one short contextual question, record the human decision.

A governed AI agent is an AI agent that uses an organisation-approved model and operates only with permitted data, tools, identities, and approval policies, where high-risk steps still require human approval.

Operating memory is the governed layer of organisational context that both people and approved AI agents may draw on: decisions, rationale, exceptions, and approved source material, each permission-aware and traceable to its source.