A copilot is an AI feature designed to assist a human rather than replace them — it suggests, drafts, summarizes, or flags, but the human reviews and decides. The paradigm is human-in-the-loop: the AI handles the tedious or drafting work, the human handles judgment, context, and approval.
GitHub Copilot for code autocomplete is the canonical example, but the pattern applies everywhere: a legal copilot that drafts contract summaries for attorney review, a support copilot that suggests replies for agents to approve, a recruiting copilot that screens resumes for a human recruiter to final-filter.
The copilot model has better adoption than full automation for most enterprise use cases. It builds trust (users can see and correct AI output), reduces risk (humans catch errors), and creates the data flywheel (corrections improve future suggestions). Start with copilot patterns before pursuing autonomous agents.
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