A copilot sits inside the tool someone already uses, a code editor, a spreadsheet, a CRM, and offers help with the task in front of them: suggesting the next line of code, drafting a formula, summarising a customer record, rather than requiring a separate chat window and a copy-paste round trip. The distinction from a general-purpose chatbot is placement: it’s context-aware because it’s already inside the workflow, not because it’s a fundamentally different kind of model.
This is now the dominant packaging for enterprise AI, more than standalone assistants, because it lowers the adoption barrier: nobody has to learn a new tool or remember to go use it, since it’s already where the work happens. It also raises the bar for differentiation, since the underlying model is often similar across competing products and the copilot’s actual value is in how well it’s wired into the specific workflow.
The organisational question a copilot raises is less “does it help” and more “who is accountable for its suggestions once someone acts on them,” particularly once a copilot moves from suggesting to actually executing changes on a person’s behalf.