In plain terms
In a cockpit the copilot reads the checklist, watches the instruments and takes over routine handling, while the captain stays in command. An AI copilot sits in the same seat inside everyday software: in the email client it drafts the reply, in the spreadsheet it writes the formula, in the code editor it proposes the next lines. The user accepts, changes or discards what it offers. Nothing goes out until the user sends it.
Why it matters
A copilot is the lowest-risk way to put generative AI to work, because a person reviews everything before it takes effect, and it needs no process redesign: people keep their tools and work faster in them. That is also its ceiling. The gain depends on each employee changing habits, so licences often go underused without training, and the minutes saved are hard to find in the accounts. The safety depends on people reading what they accept. Measure active use and results per role before renewing licences across the organisation.
Example
A law firm gives 120 lawyers a copilot inside the word processor and the email client. After three months, 45 use it every day, mainly to summarise long threads and to produce first drafts of standard letters; they report saving about four hours a week. 40 have opened it fewer than five times. The firm moves the unused licences to the teams that use it most and runs training by practice area.
Most often confused with
Copilot vs. Agent
The test is who presses the button. A copilot works turn by turn inside the user's task and hands every result back. An agent is given a goal, chooses its own steps and carries them out with tools. Many products labelled copilot now contain agent features, so read what the product may do without asking; the label will not tell you.
Origin: The word entered AI with GitHub Copilot, previewed in June 2021; Microsoft adopted it as the brand for its assistants in 2023.
Under the hood
A copilot is an application layer around a language model. It gathers context from the host application (the open document, the selected text, the mail thread, the codebase), adds retrieval from the organisation's data filtered by the user's access rights, and returns suggestions in the interface: inline completions, a chat in a side panel, or commands on selected content. Outputs are proposals that the user accepts, edits or rejects; that acceptance step is the control. Design questions: what the copilot may read, whether it can call tools that write, and how suggestions are logged. Risks: over-broad permissions that surface documents people should not see, automation bias when fluent drafts are accepted unread, and instructions injected into the content it reads. Useful measures: active users, acceptance rate and time saved per task. As a brand, Copilot covers Microsoft's assistants in Microsoft 365, Windows and other products, and GitHub Copilot for software development; other vendors use different names for the same pattern.