The common shape for AI inside a business is one assistant available to everybody. It answers general questions, it has no particular context, and after the first month usage settles to a handful of enthusiasts.
A co-seat is scoped to one role. It holds what that role needs to know, has access to what that role can already reach, and is measured against what that role is responsible for. When the person in the seat changes, the seat and its partner stay.
Why scope beats capability
A general assistant with a larger model behind it is usually worse at a specific job than a narrow one with the right context. The limiting factor in this work is rarely reasoning. It is knowing which of the business’s facts apply to the question, and a shared assistant cannot know that because it serves too many questions.
Built during onboarding
Where a client pod is built, every human role in it gets its partner defined at the same time as the role. That is a deliverable with a stated scope rather than an efficiency the firm mentions afterwards. It also means the client can see exactly what each seat is allowed to reach.