“AI employee” is useful shorthand when it describes a defined function. A procurement assistant checks documents; an office assistant sorts authorised incoming email. If a system only answers questions and saves no work, it functions more like a chatbot. A label does not give it accountability or authority.
Define a role as an operational agreement
Specify its input, sources, permitted operations and stopping conditions. “Prepare an offer comparison” is a bounded task. “Manage procurement independently” leaves many decisions unresolved, including limits, approvals, exceptions and consequences.
Separate reading, drafting and external action. Access to an invoice does not authorise payment. Finding a contact does not authorise sending a message. The application enforces these boundaries; persuasive model output cannot override them.
Example: an executive assignment
An executive asks for equipment offers. The role creates a task record, requests missing requirements and drafts a comparison table. After review, an employee selects a suitable option. The decision and its supporting evidence remain available for later inspection.
If a supplier has not replied, the assignment must not silently become complete. Distinguish waiting for evidence, ready for review, approved and rejected. These states show an executive the actual obstacle rather than an optimistic summary.
Questions to resolve before connection
- Who owns the result and who can approve it? - Where does the document or task record live? - What happens after an integration fails or a job runs twice? - How can an employee correct a mistake and resume manual work?
Multiple roles on one station
The number of AI roles does not equal the number of large models loaded concurrently. Roles can share a model while using different instructions and permissions; heavy work can run in a queue. What matters to the business is whether assignments finish on time. Design roles around processes, then test the resulting workload on the selected station.