An AI copilot responds within a human-led workflow. It can suggest text, code, analysis or next actions, but the user normally decides what to request, which suggestions to accept and when the task is complete.
Copilots provide a lower-autonomy starting point for adoption because employees can observe and correct output as it is produced. They still need appropriate data controls and evaluation, especially when fluent suggestions can conceal errors.
ELI5
An AI copilot helps a person complete a task while the person stays in charge. It can suggest text, code or next steps, but the user decides what to accept and when to act.
For example, a coding copilot may propose a function while the developer reviews and edits it before use. This human control is useful, but confident suggestions can still be wrong, so important output needs testing and suitable data protections.
How is an AI copilot different from an agent?
A copilot usually waits for interactive human direction, while a more autonomous agent can plan and execute several steps without immediate prompting.
When is a copilot preferable to an autonomous agent?
A copilot is preferable when tasks need frequent judgment, requirements are ambiguous or the organization has not yet validated unattended operation.
