12 AI Concepts Every Tech Leader Should Know

A plain-English guide to the twelve AI concepts that senior technology leaders are most often asked about, from AI agents and the Model Context Protocol to retrieval-augmented generation and context engineering. Each concept is explained without jargon and tied to the decisions a leader actually has to make about adoption, risk, and value.

From Chatbots to Agents

The first group of concepts explains the shift from systems that answer questions to systems that take action: what an AI agent is, how agentic AI differs from a chatbot, the agent loop of perceive, reason, act, and observe, and the Model Context Protocol that connects agents to tools and data.

How Models Actually Work

The second group covers the mechanics leaders need to reason about cost and reliability: large language models as the engine, tokens and context windows as the limits, retrieval-augmented generation for grounding answers in real sources, and context engineering as the discipline of giving a model the right information at the right time.

What Leaders Actually Decide

The final group connects the technology to the decisions on a leader's desk: where AI creates real value, how to assess and contain risk, where human oversight is required, and how governance keeps capability and control moving together. For the applied side, see the NIST AI RMF playbook and AI governance in the United Nations.