Operating Model & AI-Native Ops glossary
48 terms on operating models, ontologies, knowledge graphs and AI-native operations.
Frequently asked questions
What is the Operating Model & AI-Native Ops glossary?
It is a reference of 48 terms on how enterprises are designed to run and how AI is changing that, from operating models, ontologies and knowledge graphs to API-first and composable architecture, AI agents, agentic workflows and automation. Each entry gives a short definition first, then explains what the term means in practice.
Who is the Operating Model & AI-Native Ops glossary for?
Executives and transformation leaders, strategy and operations teams, enterprise and data architects, AI and platform teams, and anyone designing how their organization plans, decides and works as AI takes on more of the work.
How are the terms organized?
Terms are listed A to Z and tagged by category: operating model fundamentals; ontology and knowledge graph; headless and composable; and AI-native operations. Use the search box or the category filters to narrow the list, or jump straight to a letter.
What is the difference between an operating model and a business model?
A business model explains how a company creates, delivers and captures value, including who its customers are and how it makes money. An operating model explains how the organization runs to deliver that business model: its structure, processes, decision rights, technology, data and management routines. See operating model vs. business model.
How does this glossary fit into the Enterprise Operating Model?
It covers the Enterprise Operating Model itself and the Foundation it runs on: a shared ontology and knowledge graph that connect strategy, money, work and decisions, open interfaces that keep systems composable, and the AI agents that increasingly do the work. See the Enterprise Operating Model.